dataclysm who we are 
by christian rudder 



Copyright © 2014 by Christian Rudder
All rights reserved.
Published in the United States by Crown Publishers, an imprint of the Crown Publishing Group, a division
of Random House LLC, a Penguin Random House Company, New York.
www.crownpublishing.com
CROWN and the Crown colophon are registered trademarks of Random House LLC.
Grateful acknowledgment is made to Psychology Today Magazine for permission to reprint an excerpt from
“Final Analysis: Missed Connections” by Dorothy Gambrell (January/February 2013), copyright © 2013 by
Sussex Publishers, LLC. Reprinted by permission of Psychology Today Magazine.
Image on this page: Film still from Dazed and Confused, copyright © 1993 by Polygram Filmed
Entertainment. Reprinted by permission of Universal Studios Licensing LLC.
Table on this page: “Zipf’s Law and Vocabulary” by C. J. Sorell from The Encyclopedia of Applied
Linguistics, edited by C. A. Chapelle (Oxford: Wiley-Blackwell, 2012). Reprinted by permission of the
author.
Table on this page: Traits predicted by a Facebook user’s “likes” adapted from Figure 2, “Prediction
accuracy of classification of dichotomous/dichotomized attributes expressed by the AUC” in “Private Traits
and Attributes Are Predictable from Digital Records of Human Behavior” by Michael Kosinskia, David
Stillwell, and Thore Graepel (Washington, DC: PNAS, 2013). Reprinted by permission of the Proceedings
of the National Academy of Sciences of the United States of America.
Library of Congress Cataloging-in-Publication Data
Rudder, Christian.
Dataclysm : who we are (when we think no one’s looking) / Christian Rudder.—First Edition.
pages cm
1. Behavioral assessment. 2. Human behavior. 3. Social media. 4. Big data. I. Title. BF176.5.R83
2014
155.2′8—dc23 2014007364
ISBN 978-0-385-34737-2
Ebook ISBN 978-0-385-34738-9
Jacket design by Christopher Brand
v3.1
CONTENTS
Cover
Title Page
Copyright
Introduction
Part 1.
What Brings Us Together
1. Wooderson’s Law
2. Death by a Thousand Mehs
3. Writing on the Wall
4. You Gotta Be the Glue
5. There’s No Success Like Failure
Part 2.
What Pulls Us Apart
6. The Confounding Factor
7. The Beauty Myth in Apotheosis
8. It’s What’s Inside That Counts
9. Days of Rage
Part 3.
What Makes Us Who We Are
10. Tall for an Asian
11. Ever Fallen in Love?
12. Know Your Place
13. Our Brand Could Be Your Life
14. Breadcrumbs
Coda
A Note on the Data
Notes
Acknowledgments
Index
Introduction
You have by now heard a lot about Big Data: the vast potential, the ominous
consequences, the paradigm-destroying new paradigm it portends for mankind
and his ever-loving websites. The mind reels, as if struck by a very dull object.
So I don’t come here with more hype or reportage on the data phenomenon. I
come with the thing itself: the data, phenomenon stripped away. I come with a
large store of the actual information that’s being collected, which luck, work,
wheedling, and more luck have put me in the unique position to possess and
analyze.
I was one of the founders of OkCupid, a dating website that, over a very unbubbly long haul of ten years, has become one of the largest in the world. I
started it with three friends. We were all mathematically minded, and the site
succeeded in large part because we applied that mind-set to dating; we brought
some analysis and rigor to what had historically been the domain of love
“experts” and grinning warlocks like Dr. Phil. How the site works isn’t all that
sophisticated—it turns out the only math you need to model the process of two
people getting to know each other is some sober arithmetic—but for whatever
reason, our approach resonated, and this year alone 10 million people will use
the site to find someone.
As I know too well, websites (and founders of websites) love to throw out big
numbers, and most thinking people have no doubt learned to ignore them; you
hear millions of this and billions of that and know it’s basically “Hooray for
me,” said with trailing zeros. Unlike Google, Facebook, Twitter, and the other
sources whose data will figure prominently in this book, OkCupid is far from a
household name—if you and your friends have all been happily married for
years, you’ve probably never heard of us. So I’ve thought a lot about how to
describe the reach of the site to someone who’s never used it and who rightly
doesn’t care about the user-engagement metrics of some guy’s startup. I’ll put it
in personal terms instead. Tonight, some thirty thousand couples will have their
first date because of OkCupid. Roughly three thousand of them will end up
together long-term. Two hundred of those will get married, and many of them, of
course, will have kids. There are children alive and pouting today, grouchy little
humans refusing to put their shoes on right now, who would never have existed
but for the whims of our HTML.
I have no smug idea that we’ve perfected anything, and it’s worth saying here
that while I’m proud of the site my friends and I started, I honestly don’t care if
you’re a member or go create an account or what. I’ve never been on an online
date in my life and neither have any of the other founders, and if it’s not for you,
believe me, I get that. Tech evangelism is one of my least favorite things, and
I’m not here to trade my blinking digital beads for anyone’s precious island. I
still subscribe to magazines. I get the Times on the weekend. Tweeting
embarrasses me. I can’t convince you to use, respect, or “believe in” the Internet
or social media any more than you already do—or don’t. By all means, keep
right on thinking what you’ve been thinking about the online universe. But if
there’s one thing I sincerely hope this book might get you to reconsider, it’s
what you think about yourself. Because that’s what this book is really about.
OkCupid is just how I arrived at the story.
I have led OkCupid’s analytics team since 2009, and my job is to make sense
of the data our users create. While my three founding partners have done almost
all the hard work of actually building the site, I’ve spent years just playing with
the numbers. Some of what I work on helps us run the business: for example,
understanding how men and women view sex and beauty differently is essential
for a dating site. But a lot of my results aren’t directly useful—just interesting.
There’s not much you can do with the fact that, statistically, the least black band
on Earth is Belle & Sebastian, or that the flash in a snapshot makes a person look
seven years older, except to say huh, and maybe repeat it at a dinner party.
That’s basically all we did with this stuff for a while; the insights we gleaned
went no further than an occasional lame press release. But eventually we were
analyzing enough information that larger trends became apparent, big patterns in
the small ones, and, even better, I realized I could use the data to examine taboos
like race by direct inspection. That is, instead of asking people survey questions
or contriving small-scale experiments, which was how social science was often
done in the past, I could go and look at what actually happens when, say,
100,000 white men and 100,000 black women interact in private. The data was
sitting right there on our servers. It was an irresistible sociological opportunity.
I dug in, and as discoveries built up, like anyone with more ideas than
audience, I started a blog to share them with the world. That blog then became
this book, after one important improvement. For Dataclysm, I’ve gone far
beyond OkCupid. In fact, I’ve probably put together a data set of person-toperson interaction that’s deeper and more varied than anything held by any other
private individual—spanning most, if not all, of the significant online data
sources of our time. In these pages I’ll use my data to speak not just to the habits
of one site’s users but also to a set of universals.
The public discussion of data has focused primarily on two things:
government spying and commercial opportunity. About the first, I doubt I know
any more than you—only what I’ve read. To my knowledge, the national
security apparatus has never approached any dating site for access, and unless
they plan to criminalize the faceless display of utterly ripped abs or young
women from Brooklyn going on and on about how much they like scotch, when,
come on, you know they really don’t, I can’t imagine they’d find much of
interest. About the second story, data-as-dollars, I know better. As I was
beginning this book, the tech press was slick with drool over the Facebook IPO;
they’d collected everyone’s personal data and had been turning it into all this
money, and now they were about to turn that money into even more money in
the public markets. A Times headline from three days before the offering says it
all: “Facebook Must Spin Data into Gold.” You half expected Rumpelstiltskin to
show up on the OpEd page and be like, “Yes, America, this is a solid buy.”
As a founder of an ad-supported site, I can confirm that data is useful for
selling. Each page of a website can absorb a user’s entire experience—
everything he clicks, whatever he types, even how long he lingers—and from
this it’s not hard to form a clear picture of his appetites and how to sate them.
But awesome though the power may be, I’m not here to go over our nation’s
occult mission to sell body spray to people who update their friends about body
spray. Given the same access to the data, I am going to put that user experience
—the clicks, keystrokes, and milliseconds—to another end. If Big Data’s two
running stories have been surveillance and money, for the last three years I’ve
been working on a third: the human story.
Facebook might know that you’re one of M&M’s many fans and send you
offers accordingly. They also know when you break up with your boyfriend,
move to Texas, begin appearing in lots of pictures with your ex, and start dating
him again. Google knows when you’re looking for a new car and can show the
make and model preselected for just your psychographic. A thrill-seeking
socially conscious Type B, M, 25–34? Here’s your Subaru. At the same time,
Google also knows if you’re gay or angry or lonely or racist or worried that your
mom has cancer. Twitter, Reddit, Tumblr, Instagram, all these companies are
businesses first, but, as a close second, they’re demographers of unprecedented
reach, thoroughness, and importance. Practically as an accident, digital data can
now show us how we fight, how we love, how we age, who we are, and how
we’re changing. All we have to do is look: from just a very slight remove, the
data reveals how people behave when they think no one is watching. Here I will
show you what I’ve seen. Also, fuck body spray.
If you read a lot of popular nonfiction, there are a couple things in Dataclysm
that you might find unusual. The first is the color red. The second is that the
book deals in aggregates and big numbers, and that makes for a curious absence
in a story supposedly about people: there are very few individuals here. Graphs
and charts and tables appear in abundance, but there are almost no names. It’s
become a cliché of pop science to use something small and quirky as a lens for
big events—to tell the history of the world via a turnip, to trace a war back to a
fish, to shine a penlight through a prism just so and cast the whole pretty
rainbow on your bedroom wall. I’m going in the opposite direction. I’m taking
something big—an enormous set of what people are doing and thinking and
saying, terabytes of data—and filtering from it many small things: what your
network of friends says about the stability of your marriage, how Asians (and
whites and blacks and Latinos) are least likely to describe themselves, where and
why gay people stay in the closet, how writing has changed in the last ten years,
and how anger hasn’t. The idea is to move our understanding of ourselves away
from narratives and toward numbers, or, rather, to think in such a way that
numbers are the narrative.
This approach evolved from long toil in the statistical slag pits. Dataclysm is
an extension of what my coworkers and I have been doing for years. A dating
site brings people together, and to do that credibly it has to get at their desires,
habits, and revulsions. So you collect a lot of detailed data and work very hard to
translate it all into general theories of human behavior. What a person develops
working amidst all this information, as opposed to, say, working for the wedding
section of the Sunday paper, is a special kinship with the shambling whole of
humanity rather than with any two individuals. You grow to understand people
much as a chemist might understand, and through understanding come to love,
the swirling molecules of his tincture.
That said, all websites, and indeed all data scientists, objectify. Algorithms
don’t work well with things that aren’t numbers, so when you want a computer
to understand an idea, you have to convert as much of it as you can into digits.
The challenge facing sites and apps is thus to chop and jam the continuum of
human experience into little buckets 1, 2, 3, without anyone noticing: to divide
some vast, ineffable process—for Facebook, friendship, for Reddit, community,
for dating sites, love—into pieces a server can handle. At the same time you
have to retain as much of the je ne sais quoi of the thing as you can, so the users
believe what you’re offering represents real life. It’s a delicate illusion, the
Internet; imagine a carrot sliced so cleanly that the pieces stay there in place on
the cutting board, still in the shape of a carrot. And while this tension—between
the continuity of the human condition and the fracture of the database—can
make running a website complicated, it’s also what makes my story go. The
approximations technology has devised for things like lust and friendship offer a
truly novel opportunity: to put hard numbers to some timeless mysteries; to take
experiences that we’ve been content to put aside as “unquantifiable” and instead
gain some understanding. As the approximations have gotten better and better,
and as people have allowed them further into their lives, that understanding has
improved with startling speed. I’m going to give you a quick example, but I first
want to say that “Making the Ineffable Totally Effable” really should’ve been
OkCupid’s tagline. Alas.
Ratings are everywhere on the Internet. Whether it’s Reddit’s up/down votes,
Amazon’s customer reviews, or even Facebook’s “like” button, websites ask you
to vote because that vote turns something fluid and idiosyncratic—your opinion
—into something they can understand and use. Dating sites ask people to rate
one another because it lets them transform first impressions such as:
He’s got beautiful eyes
Hmmm, he’s cute, but I don’t like redheads
Ugh, gross
… into simple numbers, say, 5, 3, 1 on a five-star scale. Sites have collected
billions of these microjudgments, one person’s snap opinion of someone else.
Together, all those tiny thoughts form a source of vast insight into how people
arrive at opinions of one another.
The most basic thing you can do with person-to-person ratings like this is
count them up. Take a census of how many people averaged one star, two stars,
and so on, and then compare the tallies. Below, I’ve done just that with the
average votes given to straight women by straight men. This is the shape of the
curve:
Fifty-one million preferences boil down to this simple stand of rectangles. It is,
in essence, the collected male opinion of female beauty on OkCupid. It folds all
the tiny stories (what a man thinks of a woman, millions of times over) and all
the anecdotes (any one of which we could’ve expanded upon, were this a
different kind of book) into an intelligible whole. Looking at people like this is
like looking at Earth from space; you lose the detail, but you get to see
something familiar in a totally new way.
So what is this curve telling us? It’s easy to take this basic shape—a bell curve
—for granted, because examples in textbooks have probably led you to expect it,
but the scores could easily have gone hard to one side or the other. When
personal preference is involved, they often do. Take ratings of pizza joints on
Foursquare, which tend to be very positive:
Or take the recent approval ratings for Congress, which, because politicians
are the moral opposite of pizza, skew the other way:
Also, our male-to-female ratings curve is unimodal, meaning that the
women’s scores tend to cluster around a single value. This again is easy to shrug
at, but many situations have multiple modes, or “typical” values. If you plot
NBA players by how often they were in the starting lineup in the 2012–13
season, you get a bunch of athletes clustered at either end, and almost no one in
the middle:
That’s the data telling us that coaches think a given player is either good
enough to start, or he isn’t, and the guy’s in or out of the lineup accordingly.
There’s a clear binary system. Similarly, in our ratings data, men as a group
might’ve seen women as “gorgeous” or “ugly” and left it at that; like top-line
basketball talent, beauty could’ve been a you-have-it-or-you-don’t kind of thing.
But the curve we started with says something else. Looking for understanding in
data is often a matter of considering your results against these kinds of
counterfactuals. Sometimes, in the face of an infinity of alternatives, a
straightforward result is all the more remarkable for being so. In fact, our graph
is quite close to what’s called a symmetric beta distribution—a curve often
deployed to model basic unbiased decisions—which I’ll overlay here:
Our real-world data diverges only slightly (6 percent) from this formulaic
ideal, meaning this graph of male desire is more or less what we could’ve
guessed in a vacuum: it is, in fact, one of those textbook examples I was making
light of. So the curve is predictable, centered—maybe even boring. So what?
Well, this is a rare context where boringness is something special: it implies that
the individual men who did the scoring are likewise predictable, centered, and,
above all, unbiased. And when you consider the supermodels, the porn, the cover
girls, the Lara Croft–style fembots, the Bud Light ads, and, most devious of all,
the Photoshop jobs that surely these men see every day, the fact that male
opinion of female attractiveness is still where it’s supposed to be is, by my
lights, a small miracle. It’s practically common sense that men should have
unrealistic expectations of women’s looks, and yet here we see it’s just not true.
In any event, they’re far more generous than the women, whose votes go like
this:
The red chart is centered barely a quarter of the way up the scale; only one
guy in six is “above average” in an absolute sense. Sex appeal isn’t something
commonly quantified like this, so let me put it in a more familiar context:
translate this plot to IQ, and you have a world where the women think 58 percent
of men are brain damaged.
Now, the men on OkCupid aren’t actually ugly—I tested that by experiment,
pitting a random set of our users against a comparable random sample from a
social network and got the same scores for both groups—and it turns out you get
patterns like the above on every dating site I’ve seen: Tinder, Match.com,
DateHookup—sites that together cover about half the single people in the United
States. It just turns out that men and women perform a different sexual calculus.
As Harper’s put it perfectly: “Women are inclined to regret the sex they had,
and men the sex they didn’t.” You can see exactly how it works in the data. I
will add: the men above must be absolutely full of regrets.
A beta curve plots what can be thought of as the outcome of a large number of
coin flips—it traces the overlapping probabilities of many independent binary
events. Here the male coin is fair, coming up heads (which I’ll equate with
positive) just about as often as it comes up tails. But in our data we see that the
female one is weighted; it turns up heads only once every fourth flip. A large
number of natural processes, including the weather, can be modeled with betas,
and thanks to some weather bug’s obsessive archiving, I was able to compare
our person-to-person ratings to historical climate patterns. The male outlook here
is very close to the function that predicts cloud cover in New York City. The
female psyche, by the same metric, dwells in a place slightly darker than Seattle.
We’ll follow this thread through the first of Dataclysm’s three broad subjects:
the data of people connecting. Sex appeal—how it changes and what creates it—
will be our point of departure. We’ll see why, technically, a woman is over the
hill at twenty-one and the importance of a prominent tattoo, but we’ll soon move
beyond connections of the flesh. We’ll see what tweets can tell us about modern
communication, and what friendships on Facebook can say about the stability of
a marriage. Profile pictures are both a boon and a curse on the Internet: they turn
almost every service (Facebook, job sites, and, of course, dating) into a beauty
contest. We’ll take a look at what happens when OkCupid removes them for a
day and just hopes for the best. Love isn’t blind, though we find evidence it
should be.
Part 2 then looks at the data of division. We’ll begin with a close look at that
prime human divide, race—a topic we can now address at the person-to-person
level for the first time. Our privileged data exposes attitudes that most people
would never cop to in public, and we’ll see that racial bias is not only strong but
consistent—repeated almost verbatim (well, numeratim), from site to site.
Racism can be an interior thing too—just one man, his prejudice, and a
keyboard. We’ll see what Google Search has to say about the country’s most
hated word—and what that word has to say about the country. We’ll move on to
explore the divisiveness of physical beauty with a data set thousands of times
more powerful than anything previously available. Ugliness has startling social
costs that we are finally able to quantify. From there, we’ll see what Twitter
reveals about our impulse to anger. The service allows people to stay connected
up to the minute; it can drive them apart just as quickly. The collaborative rage
that it enables brings a new violence to that most ancient of human gatherings:
the mob. We’ll see if it can provide a new understanding, as well.
By the book’s third section, we will have seen the data of two people
interacting, for better and for worse; here we will look at the individual alone.
We’ll explore how ethnic, sexual, and political identity is expressed, focusing on
the words, images, and cultural markers people choose to represent themselves.
Here are five of the phrases most typical of a white woman:
my blue eyes
red hair and
four wheeling
country girl
love to be outside
Haiku by Carrie Underwood, or data? You make the call! We’ll explore
people’s public words. We’ll also see how people speak and act in private, with
an eye toward the places where labels and action diverge: bisexual men, for
example, challenge our ideas of neat identity. Next, we’ll draw on a wide range
of sources—Twitter, Facebook, Reddit, even Craigslist—to see ourselves in our
homes, both physically and otherwise. And we’ll conclude with the natural
question about a book like this: how does a person maintain his privacy in a
world where these explorations are possible?
Throughout, we’ll see that the Internet can be a vibrant, brutal, loving,
forgiving, deceitful, sensual, angry place. And of course it is: it’s made of human
beings. However, bringing all this information together, I became acutely aware
that not everyone’s life is captured in the data. If you don’t have a computer or a
smartphone, then you aren’t here. I can only acknowledge the problem, work
around it, and wait for it to go away.
I will say in the meantime that the reach of sites like Twitter and Facebook,
and even my dating data, is surprisingly thorough. If you don’t use many of
these services yourself, this is something you might not appreciate. Some 87
percent of the United States is online, and that number holds across virtually all
demographic boundaries. Urban to rural, rich to poor, black to Asian to white to
Latino, all are connected. Internet adoption is lower (around 60 percent) among
the very old and the undereducated, which is why I drew my “age line” well
short of old age in these pages—at fifty—and why I don’t address education at
all. More than 1 out of every 3 Americans access Facebook every day. The site
has 1.3 billion accounts worldwide. Given that roughly a quarter of the world is
under age fourteen, that means that something like 25 percent of adults on Earth
have a Facebook account. The dating sites in Dataclysm have registered some 55
million American members in the last three years—as I said above, that’s one
account for every two single people in the country. Twitter is an especially
interesting demographic case. It’s a glitzy tech success story, and the company is
almost single-handedly gentrifying a large swath of San Francisco. But the
service itself is fundamentally populist, both in the “openness” of its platform
and in who chooses to use it. For example, there’s no significant difference in
use by gender. People with only a high school education level tweet as much as
college graduates. Latinos use the service as much as whites, and blacks use it
twice as much. And then, of course, there’s Google. If 87 percent of Americans
use the Internet, 87 percent of them have used Google.
These big numbers don’t prove I have the complete picture of anything, but
they at least suggest that such a picture is coming. And in any event the perfect
should not be the enemy of the better-than-ever-before. The data set we’ll work
with encompasses thousands of times more people than a Gallup or Pew study;
that goes without saying. What’s less obvious is that it’s actually much more
inclusive than most academic behavioral research.
It’s a known problem with existing behavioral science—though it’s seldom
discussed publicly—that almost all of its foundational ideas were established on
small batches of college kids. When I was a student, I got paid like $25 to inhale
a slightly radioactive marker gas for an hour at Mass General and then do some
kind of mental task while they took pictures of my brain. It won’t hurt you, they
said. It’s just like spending a year in an airplane, they said. No big deal, they
said. What they didn’t say—and what I didn’t realize then—was that as I was
lying there a little hungover in some kind of CAT-scanner thing, reading words
and clicking buttons with my foot, I was standing in for the typical human male.
My friend did the study, too. He was a white college kid just like me. I’m willing
to bet most of the subjects were. That makes us far from typical.
I understand how it happens: in person, getting a real representative data set is
often more difficult than the actual experiment you’d like to perform. You’re a
professor or postdoc who wants to push forward, so you take what’s called a
“convenience sample”—and that means the students at your university. But it’s a
big problem, especially when you’re researching belief and behavior. It even has
a name. It’s called WEIRD research: white, educated, industrialized, rich, and
democratic. And most published social research papers are WEIRD.1
Several of these problems plague my data, too. It will be a while still before
digital data can scratch “industrialized” all the way off the list. But because tech
is often seen as such an “elite field”—an image that many in the industry are all
too willing to encourage—I feel compelled to distinguish between the
entrepreneurs and venture capitalists you see on technology’s public stages,
making swiping gestures and spouting buzz talk into headset mikes, people who
are usually very WEIRD indeed, from the users of the services themselves, who
are very much normal. They can’t help but be, because use of these services
—Twitter, Facebook, Google, and the like—is the norm.
As for the data’s authenticity, much of it is, in a sense, fact-checked because
the Internet is now such a part of everyday life. Take the data from OkCupid.
You give the site your city, your gender, your age, and who you’re looking for,
and it helps you find someone to meet for coffee or a beer. Your profile is
supposed to be you, the true version. If you upload a better-looking person’s
picture as your own, or pretend to be much younger than you really are, you will
probably get more dates. But imagine meeting those dates in person: they’re
expecting what they saw online. If the real you isn’t close, the date is basically
over the instant you show up. This is one example of the broad trend: as the
online and offline worlds merge, a built-in social pressure keeps many of the
Internet’s worst fabulist impulses in check.
The people using these services, dating sites, social sites, and news
aggregators alike, are all fumbling their way through life, as people always have.
Only now they do it on phones and laptops. Almost inadvertently, they’ve
created a unique archive: databases around the world now hold years of
yearning, opinion, and chaos. And because it’s stored with crystalline precision
it can be analyzed not only in the fullness of time, but with a scope and
flexibility unimaginable just a decade ago.
I have spent several years gathering and deciphering this data, not only from
OkCupid, but from almost every other major site. And yet I’ve never quite been
able to get over a nagging doubt, which, given my Luddite sympathies, pains me
all the more: writing a book about the Internet feels a lot like making a very nice
drawing about the movies. Why bother? That’s the question of my dark hours.
There’s this great documentary about Bob Dylan called Dont Look Back that I
watched a bunch back in college; my best friend, Justin, was studying film.
Somewhere in the movie, at an after-party, Bob gets into an argument with a
random guy about who did or who did not throw some glass thing in the street.
They’re both clearly drunk. The climax of the confrontation is this exchange,
and it’s stuck with me now for fifteen years:
DYLAN: I know a thousand cats who look just like you and talk just like you.
GUY AT PARTY: Oh, fuck off. You’re a big noise. You know?
DYLAN: I know it, man. I know I’m a big noise.
GUY AT PARTY: I know you know.
DYLAN: I’m a bigger noise than you, man.
GUY AT PARTY: I’m a small noise.
DYLAN: Right.
And then someone breaks it up so they can all talk poetry. It’s that kind of
night. But here’s the thing: rock star or no, big noises have been the sound of
mankind so far. Conquerors, tycoons, martyrs, saviors, even scoundrels
(especially scoundrels!)—their lives are how we’ve told our larger story, how
we’ve marked our progression from the banks of a couple of silty rivers to
wherever we are now. From Pharaoh Narmer in BCE 3100, the first living man
whose name we still know, to Steve Jobs and Nelson Mandela—the heroic
framework is how people order the world. Narmer was first on an ancient list of
kings. The scribes have changed, but that list has continued on. I mean, the
1960s, power to the people and so on, is the perfect example: that’s the era of
Lennon and McCartney, Dylan, Hendrix, not “Guy at Party.” Above all,
Everyman’s existence hasn’t been worth recording, apart from where it
intersects with a legend’s.
But this asymmetry is ending; the small noise, the crackle and hiss of the rest
of us, is finally making it to tape. As the Internet has democratized journalism,
photography, pornography, charity, comedy, and so many other courses of
personal endeavor, it will, I hope, eventually democratize our fundamental
narrative. The sound is inchoate now, unrefined. But I’m writing this book to
bring out what faint patterns I, and others, detect. This is the echo of the
approaching train in ears pressed to the rail. Data science is far from perfect—
there’s selection bias and many other shortcomings to understand, acknowledge,
and work around. But the distance between what could be and what is grows
shorter every day, and that final convergence is the day I’m writing to.
I know there are a lot of people making big claims about data, and I’m not
here to say it will change the course of history—certainly not like internal
combustion did, or steel—but it will, I believe, change what history is. With
data, history can become deeper. It can become more. Unlike clay tablets, unlike
papyrus, unlike paper, newsprint, celluloid, or photo stock, disk space is cheap
and nearly inexhaustible. On a hard drive, there’s room for more than just the
heroes. Not being a hero myself, in fact, being someone who would most of all
just like to spend time with his friends and family and live life in small ways,
this means something to me.
Now, as much as I’d like me and you and WhoBeefed81 to be right there on
the page with the president when future works treat this decade, I imagine
everyday people will always be more or less nameless, as indeed they are even
here. The best data can’t change that. But we all will be counted. When in ten
years, twenty, a hundred, someone takes the temperature of these times and
wants to understand changes—wants to see how legalizing gay marriage both
drove and reflected broader acceptance of homosexuality or how village society
in Asia was uprooted, then created again, within its large urban centers—inside
that story, even comprising its very bones, will be data from Facebook, Twitter,
Reddit, and the like. And if not, our putative writer will have failed.
I’ve tried to capture all this with my mash-up title. Kataklysmos is Greek for
the Old Testament Flood; that’s how the word “cataclysm” came to English. The
allusion has dual resonance: there is, of course, the data as unprecedented
deluge. What’s being collected today is so deep it verges on bottomless; it’s
easily forty days and forty nights of downpour to that old handful of rain. But
there’s also the hope of a world transformed—of both yesterday’s stunted
understanding and today’s limited vision gone with the flood.
This book is a series of vignettes, tiny windows looking in on our lives—what
brings us together, what pulls us apart, what makes us who we are. As the data
keeps coming, the windows will get bigger, but there’s plenty to see right now,
and the first glimpse is always the most thrilling. So to the sills, I’ll boost you
up.
1 An article in Slate noted: “WEIRD subjects, from countries that represent only about 12 percent of the
world’s population, differ from other populations in moral decision making, reasoning style, fairness, even
things like visual perception. This is because a lot of these behaviors and perceptions are based on the
environments and contexts in which we grew up.”

1. Wooderson’s Law
2. Death by a Thousand Mehs
3. Writing on the Wall
4. You Gotta Be the Glue
5. There’s No Success Like Failure
1.
Wooderson’s Law
Up where the world is steep, like in the Andes, people use
funicular railroads to get where they need to go—a pair of cable cars connected
by a pulley far up the hill. The weight of the one car going down pulls the other
up; the two vessels travel in counterbalance. I’ve learned that that’s what being a
parent is like. If the years bring me low, they raise my daughter, and, please, so
be it. I surrender gladly to the passage, of course, especially as each new
moment gone by is another I’ve lived with her, but that doesn’t mean I don’t
miss the days when my hair was actually all brown and my skin free of weird
spots. My girl is two and I can tell you that nothing makes the arc of time more
clear than the creases in the back of your hand as it teaches plump little fingers
to count: one, two, tee.
But some guy having a baby and getting wrinkles is not news. You can start
with whatever the Oil of Olay marketing department is running up the pole this
week—as I’m writing it’s the idea of “color correcting” your face with a creamy
beige paste that is either mud from the foothills of Alsace or the very essence of
bullshit—and work your way back to myths of Hera’s jealous rage. People have
been obsessed with getting older, and with getting uglier because of it, for as
long as there’ve been people and obsession and ugliness. “Death and taxes” are
our two eternals, right? And depending on the next government shutdown, the
latter is looking less and less reliable. So there you go.
When I was a teenager—and it shocks me to realize I was closer then to my
daughter’s age than to my current thirty-eight—I was really into punk rock,
especially pop-punk. The bands were basically snottier and less proficient
versions of Green Day. When I go back and listen to them now, the whole
phenomenon seems supernatural to me: grown men brought together in trios and
quartets by some unseen force to whine about girlfriends and what other people
are eating. But at the time I thought these bands were the shit. And because they
were too cool to have posters, I had to settle for arranging their album covers and
flyers on my bedroom wall. My parents have long since moved—twice, in fact.
I’m pretty sure my old bedroom is now someone else’s attic, and I have no idea
where any of the paraphernalia I collected is. Or really what most of it even
looked like. I can just remember it and smile, and wince.
Today an eighteen-year-old tacks a picture on his wall, and that wall will
never come down. Not only will his thirty-eight-year-old self be able to go back,
pick through the detritus, and ask, “What was I thinking?,” so can the rest of us,
and so can researchers. Moreover, they can do it for all people, not just one guy.
And, more still, they can connect that eighteenth year to what came before and
what’s still to come, because the wall, covered in totems, follows him from that
bedroom in his parents’ house to his dorm room to his first apartment to his
girlfriend’s place to his honeymoon, and, yes, to his daughter’s nursery. Where
he will proceed to paper it over in a billion updates of her eating mush.
A new parent is perhaps most sensitive to the milestones of getting older. It’s
almost all you talk about with other people, and you get actual metrics at the
doctor’s every few months. But the milestones keep coming long after
babycenter.com and the pediatrician quit with the reminders. It’s just that we
stop keeping track. Computers, however, have nothing better to do; keeping
track is their only job. They don’t lose the scrapbook, or travel, or get drunk, or
grow senile, or even blink. They just sit there and remember. The myriad phases
of our lives, once gone but to memory and the occasional shoebox, are becoming
permanent, and as daunting as that may be to everyone with a drunk selfie on
Instagram, the opportunity for understanding, if handled carefully, is selfevident.
What I’ve just described, the wall and the long accumulation of a life, is what
sociologists call longitudinal data—data from following the same people, over
time—and I was speculating about the research of the future. We don’t have
these capabilities quite yet because the Internet, as a pervasive human record, is
still too young. As hard as it is to believe, even Facebook, touchstone and
warhorse that it is, has only been big for about six years. It’s not even in middle
school! Information this deep is still something we’re building toward, literally,
one day at a time. In ten or twenty years, we’ll be able to answer questions
like … well, for one, how much does it mess up a person to have every moment
of her life, since infancy, posted for everyone else to see? But we’ll also know so
much more about how friends grow apart or how new ideas percolate through
the mainstream. I can see the long-term potential in the rows and columns of my
databases, and we can all see it in, for example, the promise of Facebook’s
Timeline: for the passage of time, data creates a new kind of fullness, if not
exactly a new science.
Even now, in certain situations, we can find an excellent proxy, a sort of flashforward to the possibilities. We can take groups of people at different points in
their lives, compare them, and get a rough draft of life’s arc. This approach
won’t work with music tastes, for example, because music itself also evolves
through time, so the analysis has no control. But there are fixed universals that
can support it, and, in the data I have, the nexus of beauty, sex, and age is one of
them. Here the possibility already exists to mark milestones, as well as lay bare
vanities and vulnerabilities that were perhaps till now just shades of truth. So
doing, we will approach a topic that has consumed authors, painters,
philosophers, and poets since those vocations existed, perhaps with less art
(though there is an art to it), but with a new and glinting precision. As usual, the
good stuff lies in the distance between thought and action, and I’ll show you
how we find it.
I’ll start with the opinions of women—all the trends below are true across my
sexual data sets, but for specificity’s sake, I’ll use numbers from OkCupid. This
table lists, for a woman, the age of men she finds most attractive. If I’ve
arranged it unusually, you’ll see in a second why.
Reading from the top, we see that twenty- and twenty-one-year-old women
prefer twenty-three-year-old guys; twenty-two-year-old women like men who
are twenty-four, and so on down through the years to women at fifty, who we
see rate forty-six-year-olds the highest. This isn’t survey data, this is data built
from tens of millions of preferences expressed in the act of finding a date, and
even from just following along the first few entries, the gist of the table is clear:
a woman wants a guy to be roughly as old as she is. Pick an age in black under
forty, and the number in red is always very close. The broad trend comes
through better when I let lateral space reflect the progression of the values in red:
That dotted diagonal is the “age parity” line, where the male and female years
would be equal. It’s not a canonical math thing, just something I overlaid as a
guide for your eye. Often there is an intrinsic geometry to a situation—it was the
first science for a reason—and we’ll take advantage wherever possible.1 This
particular line brings out two transitions, which coincide with big birthdays. The
first pivot point is at thirty, where the trend of the red numbers—the ages of the
men—crosses below the line, never to cross back. That’s the data’s way of
saying that until thirty, a woman prefers slightly older guys; afterward, she likes
them slightly younger. Then at forty, the progression breaks free of the diagonal,
going practically straight down for nine years. That is to say, a woman’s tastes
appear to hit a wall. Or a man’s looks fall off a cliff, however you want to think
about it. If we want to pick the point where a man’s sexual appeal has reached its
limit, it’s there: forty.
The two perspectives (of the woman doing the rating and of the man being
rated) are two halves of a whole. As a woman gets older, her standards evolve,
and from the man’s side, the rough 1:1 movement of the red numbers versus the
black implies that as he matures, the expectations of his female peers mature as
well—practically year-for-year. He gets older, and their viewpoint
accommodates him. The wrinkles, the nose hair, the renewed commitment to
cargo shorts—these are all somehow satisfactory, or at least offset by other
virtues. Compare this to the free fall of scores going the other way, from men to
women.
This graph—and it’s practically not even a graph, just a table with a couple
columns—makes a statement as stark as its own negative space. A woman’s at
her best when she’s in her very early twenties. Period. And really my plot
doesn’t show that strongly enough. The four highest-rated female ages are
twenty, twenty-one, twenty-two, and twenty-three for every group of guys but
one. You can see the general pattern below, where I’ve overlaid shading for the
top two quartiles (that is, top half) of ratings. I’ve also added some female ages
as numbers in black on the bottom horizontal to help you navigate:
Again, the geometry speaks: the male pattern runs much deeper than just a
preference for twenty-year-olds. And after he hits thirty, the latter half of our age
range (that is, women over thirty-five) might as well not exist. Younger is better,
and youngest is best of all, and if “over the hill” means the beginning of a
person’s decline, a straight woman is over the hill as soon as she’s old enough to
drink.
Of course, another way to put this focus on youth is that males’ expectations
never grow up. A fifty-year-old man’s idea of what’s hot is roughly the same as
a college kid’s, at least with age as the variable under consideration—if
anything, men in their twenties are more willing to date older women. That
pocket of middling ratings in the upper right of the plot, that’s your “cougar”
bait, basically. Hikers just out enjoying a nice day, then bam.
In a mathematical sense, a man’s age and his sexual aims are independent
variables: the former changes while the latter never does. I call this Wooderson’s
law, in honor of its most famous proponent, Matthew McConaughey’s character
from Dazed and Confused.
Unlike Wooderson himself, what men claim they want is quite different from
the private voting data we’ve just seen. The ratings above were submitted
without any specific prompt beyond “Judge this person.” But when you ask men
outright to select the ages of women they’re looking for, you get much different
results. The gray space below is what men tell us they want when asked:
Since I don’t think that anyone is intentionally misleading us when they give
OkCupid their preferences—there’s little incentive to do that, since all you get
then is a site that gives you what you know you don’t want—I see this as a
statement of what men imagine they’re supposed to desire, versus what they
actually do. The gap between the two ideas just grows over the years, although
the tension seems to resolve in a kind of pathetic compromise when it’s time to
stop voting and act, as you’ll see.
The next plot (the final one of this type we’ll look at) identifies the age with
the greatest density of contact attempts. These most-messaged ages are
described by the darkest gray squares drifting along the left-hand edge of the
larger swath. Those three dark verticals in the graph’s lower half show the jumps
in a man’s self-concept as he approaches middle age. You can almost see the
gears turning. At forty-four, he’s comfortable approaching a woman as young as
thirty-five. Then, one year later … he thinks better of it. While a nine-year age
difference is fine, ten years is apparently too much.
It’s this kind of calculated no-man’s-land—the balance between what you
want, what you say, and what you do—that real romance has to occupy: no
matter how people might vote in private or what they prefer in the abstract, there
aren’t many fifty-year-old men successfully pursuing twenty-year-old women.
For one thing, social conventions work against it. For another, dating requires
reciprocity. What one person wants is only half of the equation.
When it comes to women seizing the initiative and reaching out to men,
because of the female-to-male attraction ratio we saw at the beginning of the
chapter (1 year:1 year), plus the nonphysical motivations that push women
toward older men—economics, for example—women send more, rather than
fewer, messages to a man as he gets older, up until the early thirties. From there,
the amount of contact declines, but no faster than the general number of
available females itself is shrinking. Think about it like this: imagine you could
take a typical twenty-year-old guy, who’s just starting to date as an adult
(definition: no SOLO cups present during at least one of
courtship/consummation/breakup), and you could somehow note all the women
who would be interested in him. If you could then track the whole lot over time,
the main way he’ll lose options from that set is when some of them just stop
being single because they’ve paired off with someone else. In fact, his total
“interested” pool would actually gain women, because as he gets older, and
presumably richer and more successful, those qualities draw younger women in.
In any event, his age, of itself, doesn’t hurt him. Over the first two decades of his
dating life, as he and the women in his pool mature, the ones who are still
available will find him as desirable an option as they did when they were all
twenty.
If you could do the same thing for a typical woman at twenty, you’d get a
different story. Over the years, she, too, would lose men from her pool to things
like marriage, but she would also lose options to time itself—as the years passed,
fewer and fewer of the remaining single men would find her attractive. Her
dating pool is like a can with two holes—it drains on the double.
The number of single men shrinks rapidly by age: per the US Census there are
10 million single men ages twenty to twenty-four, but only 5 million at thirty to
thirty-four, and just 3.5 million at forty to forty-four. When you overlay the
preference patterns we see above to those shrinking demographics, you can get a
sense of how a woman’s real options change over time. For a woman at twenty,
this is the actual shape of the dating pool:
Her peers (guys in their early twenties) form the biggest component, and the
numbers slope off rapidly—thirty-year-old men, for example, make up only a
small part. They are less likely to actually contact someone so young, despite
their privately expressed interest, and in addition many men have already
partnered off by that age. By the time the woman is fifty, this is who’s left (and
still interested), presented on the same scale. It’s Bridget Jones in charts.
Comparing the areas, for every 100 men interested in that twenty-year-old,
there are only 9 looking for someone thirty years older. Here’s the full
progression of charts like the two above, rendered from a woman’s perspective
for each of the ages twenty to fifty:
So often in my line of work, I’ll see two individuals, both alone but for
whatever reason not connecting. In this case, for this facet of the experience, it’s
two whole groups of people searching for each other at cross-purposes. Women
want men to age with them. And men always head toward youth. A thirty-twoyear-old woman will sign up, set her age-preference filters at 28–35, and begin
to browse. That thirty-five-year-old man will come along, set his filters to 24–
40, and yet rarely contact anyone over twenty-nine. Neither finds what they are
looking for. You could say they’re like two ships passing in the night, but that’s
not quite right. The men do seem at sea, pulled to some receding horizon. But in
my mind I see the women still on solid ground, ashore, just watching them
disappear.
1 This, in my opinion, is what distinguishes a true data visualization from, say, a plain graph or an
impressionistic work of art that happens to include numbers. In a visualization, the physical space itself
communicates relationships.
2.
Death by a Thousand Mehs
In 2002, the Oscars hired the director Errol
Morris to shoot a short film about why we love the movies. The Academy
wanted to kick off the telecast with a rapid-fire montage of people, both
celebrities and not, talking about their favorite films. My friend Justin was
Morris’s casting director, so he got me on the list. There was no guarantee that
I’d end up in the final cut of the short, but I could do the interview on-camera
and see how it went.
Having an in, I got scheduled the same day as the biggest names: Donald
Trump, Walter Cronkite, Iggy Pop, Al Sharpton, Mikhail Gorbachev. Trump and
Gorbachev were back to back, and somewhere out there there’s a picture of the
two of them, with me in the middle, photobombing before photobombing was a
thing. I say “somewhere” because right after the flash, Trump snapped his
fingers, and his bodyguard took Justin’s camera. For his favorite movie, Trump
picked King Kong, because he of course likes apes who try to “conquer New
York.” Gorbachev, through a translator whose mustache must’ve weighed ten
pounds, chose Gladiator. At 2:01 in Morris’s film, the wide eyes and the voice
saying “The Omen” are mine.
Now, I like a good Antichrist movie more than most people, but I chose The
Omen more or less at random. There are so many good movies, I’m actually not
sure what my favorite one is. But I know my least favorite film with absolute
certainty. Pecker, by John Waters. I walked out of it. Twice. I went once with
some friends, couldn’t deal with the mondo-trasho vibe, not to mention the
exaggerated accents, and just had to leave. The next weekend, some other
friends were going and since John Waters is a respected auteur, and hey I’m a
cool guy who gets it, I figured there was at least some chance I was wrong the
first time. Also I had nothing else to do. So I went again.
Such is the temporary madness of being twenty-two. I’m not saying John
Waters makes objectively bad movies—they’re just not for me. Or for a lot of
people. And he embraces that fact, the rejection—it’s practically his calling card
as a director. Let me put it this way: nobody leaves Pecker thinking it was
“meh”; either you loved it, or got the hell out after twenty minutes like I did,
twice. That’s by design.1
Waters’s fans seem to love him all the more for being fewer in number. On
OkCupid, a search through users’ profile text returns more results for his name
than George Lucas’s and Steven Spielberg’s combined. On Reddit, he has his
own devoted page: /r/JohnWaters,2 and while it’s not the most-trafficked URL
ever, people actually put stuff there: news, old clips, questions about him,
comments, and so on. There’s a /r/GeorgeLucas, too: it has one post, ever. If you
enter /r/StevenSpielberg into your address bar, you get “there doesn’t seem to be
anything here” from Reddit’s server because, as good as his work is, no one’s
been enthusiastic enough to make a page. Even highly Internet-friendly directors
like J. J. Abrams don’t have their own page. It takes a certain special motivation
to, say, make a fan site, and that motivation is often intensified by feeling like
you’re part of a special, embattled elect. Devotion is like vapor in a piston—
pressure helps it catch.
Like many artists before and since, Waters understands exactly how it works:
repelling some people draws others all the closer, and I bring him up not only
because of my lifelong personal struggle with Pecker, but because Waters also
gets the universality of the principle: it’s not just true for art. He’s got a lot of
great quotes, but here’s one that speaks right to me: “Beauty is looks you can
never forget. A face should jolt, not soothe.” He’s completely correct, for as with
music, as with movies, and as with a wide variety of human phenomena: a flaw
is a powerful thing. Even at the person-to-person level, to be universally liked is
to be relatively ignored. To be disliked by some is to be loved all the more by
others. And, specifically, a woman’s overall sex appeal is enhanced when some
men find her ugly.
You can see this in the profile ratings on OkCupid. Because the site’s rating
system is 5 stars, the votes have more depth than just a yes or a no. People give
degrees of opinion, and that gives us room to explore. To show this finding,
we’ll have to go on a short mathematical journey. These kinds of exercises are
what make data science work. To put together puzzles, you have to lay out all
the pieces and then just start trying things. In the absence of careful sifting,
reduction, and parsimony, very little just “jumps out at you” from terabytes of
raw data.
Consider a group of women with approximately the same attractiveness, let’s
just say the ones rated in the middle:
Now imagine a woman in that group and think of the many different votes
men could’ve given her—basically think about how she ended up in the middle.
There are thousands of possibilities; here are just a few I made up, combinations
of 1s, 2s, 3s, 4s, and 5s, which all come to an average of 3:
As you might’ve noticed, the vote patterns I’ve chosen get more polarized as
they go from Pattern A to Pattern E. Each row still averages out to that same
central “3,” but they express that average in different ways. Pattern A is the
embodiment of consensus. There, the men who cast the votes have spoken in
perfect unison: this woman is exactly in the middle. But by the time we get to the
bottom of the table, the overall average is still centered, yet no single individual
actually holds that central opinion. Pattern E shows the most extreme possible
path to a middling average: for every man awarding our theoretical woman a
“1,” someone else gives her a “5,” and the total result comes out to a “3” almost
in spite of itself. That’s the John Waters way.
These patterns exemplify a mathematical concept called variance. It’s a
measure of how widely data is scattered around a central value. Variance goes
up the further the data points fall from the average; in the table above, it is
highest in Pattern E. One of the most common applications of variance is to
weigh volatility (and therefore risk) in financial markets. Consider these two
companies:
Both returned 10 percent for the year, but they are very different investments.
Associated Widgets experienced large swings in value throughout the year,
while Widgets Inc. grew little by little, showing consistent gains each month.
Computing the variance allows analysts to capture this distinction in one simple
number, and all other things being equal, investors much prefer the low score of
that pattern on the right. Same return, fewer heart palpitations. Of course, when
it comes to romance, heart palpitations are the return, and that gets to the crux of
it. It turns out that variance has almost as much to do with the sexual attention a
woman gets as her overall attractiveness.
In any group of women who are all equally good-looking, the number of
messages they get is highly correlated to the variance: from the pageant queens
to the most homely women to the people right in between, the individuals who
get the most affection will be the polarizing ones. And the effect isn’t small—
being highly polarizing will in fact get you about 70 percent more messages.
That means variance allows you to effectively jump several “leagues” up in the
dating pecking order—for example, a very low-rated woman (20th percentile)
with high variance in her votes gets hit on about as much as a typical woman in
the 70th percentile.
Part of that is because variance means, by definition, that more people like
you a lot (as well as dislike you a lot). And those enthusiastic guys—let’s just
call them the fanboys—are the ones who do most of the messaging. So by
pushing people toward the high end (the 5s), you get more action.
But the negative votes themselves are part of the story, too. They drive some
of the attention on their own. For example, the real patterns exemplified by C
and D below get about 10 percent more messages than the ones shown in A and
B, even though the top two women are rated far better overall:
I’ve been talking about messages as if they’re an end unto themselves, but on
a dating site, messages are the precursor to outcomes like in-depth
conversations, the exchange of contact information, and eventually in-person
meetings. People with higher variance get more of all these things, too. So, for
example, woman D above would have about 10 percent more conversations, 10
percent more dates, and, likely, 10 percent more sex than woman A, even though
in terms of her absolute rating she’s much less attractive.
Moreover, the men giving out those 1s and 2s are not themselves hitting on
the women—people practically never contact someone they’ve rated poorly.3
It’s that having haters somehow induces everyone else to want you more. People
not liking you somehow brings you more attention entirely on its own. And, yes,
in his underground castle, Karl Rove smiles knowingly, petting an enormous
toad.
It only adds to the mystery of the phenomenon that OkCupid doesn’t publish
raw attractiveness scores (or a variance number, of course) for anyone on the
site. Nobody is consciously making decisions based on this data. But people
have a way of feeling the math behind things, whether they’re aware of it or not,
and here’s what I think is going on. Suppose a guy is attracted to a woman he
knows is unconventional-looking. Her very unconventionality implies that some
other men are likely turned off; it means less competition. Having fewer rivals
increases his chances of success. I can imagine our man browsing her profile,
circling his cursor, thinking to himself: I bet she doesn’t meet many guys who
think she’s awesome. In fact, I’m actually into her for her quirks, not in spite of
them. This is my diamond in the rough, and so on. To some degree, her very
unpopularity is what makes her attractive to him. And if our browsing guy was
at all on the fence about whether to actually introduce himself, this might make
the difference.
Looking at the phenomenon from the opposite angle—the low-variance side
—a relatively attractive woman with consistent scores is someone any guy
would consider conventionally pretty. And she therefore might seem to be more
popular than she really is. Broad appeal gives the impression that other guys are
after her, too, and that makes her incrementally less appealing. Our interested
but on-the-fence guy moves on.
This is my theory at least. But the idea that variance is a positive thing is fairly
well established in other arenas. Social psychologists call it the “pratfall
effect”—as long as you’re generally competent, making a small, occasional
mistake makes people think you’re more competent. Flaws call out the good
stuff all the more. This need for imperfection might just be how our brains are
put together. Our sense of smell, which is the most connected to the brain’s
emotional center, prefers discord to unison. Scientists have shown this in labs,
by mixing foul odors with pleasant ones, but nature, in the wisdom of
evolutionary time, realized it long before. The pleasant scent given off by many
flowers, like orange blossoms and jasmine, contains a significant fraction (about
3 percent) of a protein called indole. It’s common in the large intestine, and on
its own, it smells accordingly. But the flowers don’t smell as good without it. A
little bit of shit brings the bees. Indole is also an ingredient in synthetic human
perfumes.
You can see a public implementation, as it were, of the OkCupid data in the
rarefied world of modeling. The women are all professionally gorgeous—5 stars
out of 5, of course. But even at that high level it’s still about distinguishing
yourself through imperfection. Cindy Crawford’s career took off after she
stopped covering her mole. Linda Evangelista had the severe hair—you can’t say
it made her prettier, but it did make her far more interesting. Kate Upton, at least
according to the industry standard, has a few extra pounds. Pulling a few
examples from the data set, perhaps ones that are more relatable than swimsuit
models, will help you see how it works for a normal person. Here are six
women, all with middle-of-the-road overall scores, but who tend to get extreme
reactions either way: lots of Yes, lots of No, but very little Meh:
Thanks to each of them for having the confidence to agree to be displayed and
discussed here. What you see in the array is what you get throughout the corpus.
These are people who’ve purposefully abandoned the middle road: with body
art, a snarky expression, or by eating a grilled cheese like a badass. And you find
many relatively normal women with an unusual trait: like the center woman in
the bottom row, whose blue hair you can’t see in black and white. And you
especially see women who’ve chosen to play up their particular asset/liability. If
you can pull off, say, a 3.3 rating despite the extra pounds or the people who
hate tattoos or whatever, then, literally, more power to you.
So at the end of it, given that everyone on Earth has some kind of flaw, the
real moral here is: be yourself and be brave about it. Certainly trying to fit in,
just for its own sake, is counterproductive. I know this is dangerously close to
the kind of thing that gets put on a quilt, and quilts, being the PowerPoint
presentations of an earlier time, are the opposite of science. It also sounds a lot
like the advice a mother gives, along with a pat on the head, to her big-nosed and
brace-faced son when he’s fourteen and can’t figure out why he isn’t more
popular. But either way, there it is, in the numbers. Like I said, people can feel
the math behind things, especially, thankfully, moms. I just wish she’d told me
that by ninth grade bears aren’t cool.
1 Waters on film: “To me, bad taste is what entertainment is all about. If someone vomits while watching
one of my films, it’s like getting a standing ovation.”
2 These pages on Reddit are called subreddits. I’ll explain the site and its nuances in more detail later.
3 Only 0.2 percent of the messages on the site are sent by users to a person to whom they awarded fewer
than 3 stars.

3.
Writing on the Wall
Nostalgia used to be called mal du Suisse—the Swiss
sickness. Their mercenaries were all over Europe and were apparently notorious
for wanting to go home. They would get misty and sing shepherd ballads instead
of fighting, and when you’re the king of France with Huguenots to burn, songs
won’t do. The ballads were banned. In the American Civil War nostalgia was
such a problem it put some 5,000 troops out of action, and 74 men died of it—at
least according to army medical records. Given the circumstances, being sad to
death is actually kind of understandable, but then again, this was also the time of
leeches and the bonesaw, so who knows what was really going on. It’s
interesting to think that in those days, many of the people who left home did so
to go to war—much of the early literature on nostalgia, which was seen then as a
bona fide disease, mentions soldiers. In that sepia-toned way I can’t help but
think about the past, I like to imagine scientists in 1863, on either side of the
Potomac, working furiously against the clock to develop the ultimate war-ending
superweapon: high school yearbooks.
I actually don’t even know if they have high school yearbooks anymore. It’s
hard to see why you’d need one now that Facebook’s around, although
according to the company’s last quarterly report, people under eighteen aren’t
using Facebook as much as they used to. So maybe the kids need the printed
copy again, I don’t know.1 But however teenagers are staying in touch—whether
it’s through Snapchat or WhatsApp or Twitter—I’m positive they’re doing it
with words. Pictures are part of the appeal of all of these services, obviously, but
you can only say so much without a keyboard. Even on Instagram, the comments
and the captions are essential—the photo after all is just a few inches square. But
the words are the words are the words. They’re still how feelings come across
and how connections are made.
In fact, for all the hand-wringing over technology’s effect on our culture, I am
certain that even the most reticent teenager in 2014 has written far more in his
life than I or any of my classmates had back in the early ’90s. Back then, if you
needed to talk to someone you used the phone. I wrote a few stiff thank-you
notes and maybe one letter a year. The typical high school student today must
surpass that in a morning. The Internet has many regrettable sides to it, but that’s
one thing that’s always stood it in good stead with me: it’s a writer’s world.
Your life online is mediated through words. You work, you socialize, you flirt,
all by typing. I honestly feel there’s a certain epistolary, Austenian grandness to
the whole enterprise. No matter what words we use or how we tap out the letters,
we’re writing to one another more than ever. Even if sometimes
dam gerl
is all we have to say.
Major Sullivan Ballou was one of the soldiers in the Union army, on the
Potomac, suffering, and homesick. Early in Ken Burns’s The Civil War, a
narrator reads his farewell letter to his wife, to his “very dear Sarah,” and it’s a
moving and important moment in the film. The Major was writing from camp
before the first large battle of the war, and he was mortally wounded days later.
His words were the last his family would ever hear from him, and they drove
home the greater sorrow the nation would face in the years to come. Because of
the exposure, the Ballou letter has become one of the most famous ever written
—when I search for “famous letter,” Google lists it second. It’s a beautiful piece
of writing, but think of all the other letters that will never be read aloud, that
were burned, lost in some shuffle, or carried off by the wind, or that just
moldered away.
Today we don’t have to rely on the lucky accident of preservation to know
what someone was thinking or how he talked, and we don’t need the one to stand
in for the many. It’s all preserved, not just one man to one wife before one battle,
but all to all, before and after and even in the middle of each of our personal
battles. You can find readings of the Ballou letter on YouTube, and many of the
comments are along the lines of “They just don’t make them like that anymore.”
That’s true. But what they, or rather we, are making offers a richness and a
beauty of a different kind: a poetry not of lyrical phrases but of understanding.
We are at the cusp of momentous change in the study of human communication
and what it tries to foster: community and personal connection.
When you want to learn about how people write, their unpolished, unguarded
words are the best place to start, and we have reams of them. There will be more
words written on Twitter in the next two years than contained in all books ever
printed. It’s the epitome of the new communication: short and in real time.
Twitter was, in fact, the first service not only to encourage brevity and
immediacy, but to require them. Its prompt is “What’s happening?” and it gives
users 140 characters to tell the world. And Twitter’s sudden popularity, as much
as its sudden redefinition of writing, seemed to confirm the fear that the Internet
was “killing our culture.” How could people continue to write well (and even
think well) in this new confined space—what would become of a mind so
restricted? The actor Ralph Fiennes spoke for many when he said, “You only
have to look on Twitter to see evidence of the fact that a lot of English words
that are used, say, in Shakespeare’s plays or P. G. Wodehouse novels … are so
little used that people don’t even know what they mean now.”
Even basic analysis shows that language on Twitter is far from a degraded
form. Below, I’ve compared the most common words on Twitter against the
Oxford English Corpus—a collection of nearly 2.5 billion words of modern
writing of all kinds—journalism, novels, blogs, papers, everything. The OEC is
the canonical census of the current English vocabulary. I’ve charted only the top
100 words out of the tens of thousands that people use, which may seem like a
paltry sample, but roughly half of all writing is formed from these words alone
(both on Twitter and in the OEC). The most important thing to notice on
Twitter’s list is this: despite the grumblings from the weathered sentinels atop
Fortress English, there are only two “netspeak” entries—rt, for “retweet” and u,
for “you”—in the top 100. You’d think that contractions, grammatical or
otherwise, would be staples of a form that only allows a person 140 characters,
but instead people seem to be writing around the limitation rather than
stubbornly through it. Second, when you calculate the average word length of
the Twitter list, it’s longer than the OEC’s: 4.3 characters to 3.4. And look
beyond length to the content of the Twitter vocabulary. I’ve highlighted the
words unique to it in order to make the comparison easier:
OEC Twitter OEC Twitter
1 the to 51 when back
be a make an
to i can see
of the like more
and and time by
a in no today
in you just twitter
that my him or
have for know as
10 I on 60 take make
it
o
f
p
e
o
ple
w
h
o
f
o
r it in
t
o
g
o
t
n
o
t
m
e
y
e
a
r
h
e
r
e
o
n
t
his
y
o
u
r
w
a
n
t
wit
h
wit
h
g
o
o
d
n
e
e
d
h
e
a
t
s
o
m
e
h
a
p
p
y
a
s
j
u
s
t
c
o
uld
t
o
o
y
o
u
s
o
t
h
e
m
u
d
o
b
e
s
e
e
b
e
s
t
2
0
a
t
r
t
7
0
o
t
h
e
r
p
e
o
ple
t
his
o
u
t
t
h
a
n
s
o
m
e
b
u
t
t
h
a
t
t
h
e
n
t
h
e
y
his
h
a
v
e
n
o
w lif
e
b
y
y
o
u
r lo
o
k
t
h
e
r
e
f
r
o
m
all o
nly
t
hin
k
t
h
e
y
u
p
c
o
m
e
g
oin
g
w
e lo
v
e it
s
w
h
y
s
a
y
d
o
o
v
e
r
h
e
h
e
r
w
h
a
t
t
hin
k
r
e
ally
3
0
s
h
e lik
e
8
0
als
o
w
a
y
o
r
n
o
t
b
a
c
k
c
o
m
e
a
n
g
e
t
a
f
t
e
r
m
u
c
h
will n
o
u
s
e
o
nly
m
y
g
o
o
d
t
w
o
o
f
f
o
n
e
b
u
t
h
o
w
s
till all new our righ
t
w
o
uld
c
a
n
w
o
r
k
nig
h
t
t
h
e
r
e if
fir
s
t
h
o
m
e
t
h
eir
d
a
y
w
ell s
a
y
4
0
w
h
a
t
n
o
w
9
0
w
a
y
g
r
e
a
t
s
o
tim
e
e
v
e
n
n
e
v
e
r
u
p
f
r
o
m
n
e
w
w
o
r
k
o
u
t
g
o
w
a
n
t
w
o
uld if how because last about we any first who will these over get one give take which about day its go know most better
50 me when 100 us them
While the OEC list is rather drab, lots of helpers and modifiers—workmanlike
language to get you to some payoff noun or verb—on Twitter, there’s no room
for functionaries; every word’s gotta be boss. So you see vivid stuff like:
love
happy
life
today
best
never
home
… make the top 100 cut. Twitter actually may be improving its users’ writing, as
it forces them to wring meaning from fewer letters—it embodies William
Strunk’s famous dictum, Omit needless words, at the keystroke level. A person
tweeting has no option but concision, and in a backward way the character limit
actually explains the slightly longer word length we see. Given finite room to
work, longer words mean fewer spaces between them, which means less waste.
Although the thoughts expressed on Twitter may be foreshortened, there’s no
evidence here that they’re diminished.
Mark Liberman, a professor of linguistics at the University of Pennsylvania,
concluded much the same thing: in a direct response to Mr. Fiennes, he
calculated the typical word length in Hamlet (3.99) and in a collection of
Wodehouse’s stories (4.05) and found them both less than the length in his
Twitter sample (4.80).2 He’s just one of many comparative linguists who’ve
begun mining Twitter’s data. A team at Arizona State was able to reach beyond
word count and length, and into the sentiment and style of the writing, and they
found several surprising things: first, Twitter does not change how a person
writes. Among the many examples they tracked, if a writer uses “u” for the
second person in e-mails or text messages, she will also use it on Twitter. But,
likewise, if she generally spells out “you,” she does so everywhere—on Twitter,
in texts, in e-mail, and so on. The decision to refer to the first-person singular as
“I” or “i” follows the same pattern. That is, a person’s style doesn’t change from
medium to medium; there is no “dumbing down.” You write how you write,
wherever you write. The linguists also measured Twitter’s lexical density, its
proportion of content-carrying words like verbs and nouns, and found it was not
only higher than e-mail’s, but was comparable to the writing on Slate, the
control used for magazine-level syntax. Everything points to the same
conclusion: that Twitter hasn’t so much altered our writing as just gotten it to fit
into a smaller place. Looking through the data, instead of a wasteland of cut
stumps, we find a forest of bonsai.
This kind of in-depth analysis (lexical density, word frequency) hints at the
real nature of the transformation under way. The change Twitter has wrought on
language itself is nothing compared with the change it is bringing to the study of
language. Twitter gives us a sense of words not only as the building blocks of
thought but as a social connector, which indeed has been the purpose of
language since humanity hunched its way across the Serengeti. And unlike older
media, Twitter gives us a way to track those bonds on an individual level. You
can see not only what a person says, but who she says it to, when, and how
often. Comparative linguists have long traced group commonalities through
language. Basic words often share common sounds (like tres, trois, drei, three,
and thran, from Spanish, French, German, English, and India’s Gujarati) and
those stems have given us a sense of the movements of genes and culture across
the face of time. Researchers are already grouping people by the language they
use on Twitter. Here I’ve excerpted an early attempt to find the tribes and
emerging dialects—this is from a corpus of 189,000 tweeters sending 75 million
tweets among them.
subgroups on Twitter by messaging pattern
example words characteristic speech
percent of
sample
nigga, poppin, chillin
shortened endings (e.g., -er => -a or -ing =>
-in)
14
tweetup, metrics,
innovation
tech buzzspeak 12
inspiring, webinar, affiliate,
tips
marketing self-help 11
etsy, adorable, hubby crafting lingo 5
pelosi, obamacare, beck,
libs
partisan talking points 4
bieber, pleasee, youu, <33 lengthened endings (repeated last letter) 2
anipals, pawesome, furever animal-based puns 1
kstew, robsessed,
kstew, robsessed,
twilighters
amalgamations/puns around the Twilight
movies
1
It’s important to note that the study grouped users by their words alone, who
they messaged, and what they wrote—these language clusters were not
determined a priori. The top-listed group is in fact the largest the researchers
detected, and it also happens to be the most voluble (sending the most tweets per
capita) as well as the most insular. Some 90 percent of the tweets sent by the
group are directed within it, and its users’ language is most strongly
“characteristic”—half of their 100 most representative words fit the “shortened
endings” pattern. Throughout the list you see groups typified by slang, pop
culture references, jargon, goofy puns—people drawn together by special ways
of speaking, and it’s exactly the kind of language (and information) that until
now has been lost to history. Like knowing a man’s last words to his wife,
knowing how people talk among friends gives you a much deeper sense of who
they are. Technocrats, political wonks, marketing gurus, the robsessed; it will be
interesting in the coming years to see how all these groups merge and
recombine, and we’ll be able to track it all through their text.
Once language and data come together, it’s that extra dimension, time, that’s
so compelling. Going forward, services like Twitter will be indispensable.
Looking back, Google Books is working to repair our historical blind spot: in
collaboration with libraries around the world, they have digitized 30 million
unique books, great and small, and, true to their expertise, they have made the
whole searchable. This body of data has created a new field of quantitative
cultural studies called culturomics; its primary method is to track changes in
word use through time. The long reach of the data (it goes back to 1800) allows
an unusual look at people and what’s important to them. Here’s a little chart I
like to call Pizza Now, Pizza Forever:
You can read bits of nonculinary history in the data, too. “Ice cream” took off
in the 1910s—right when GE introduced the powered home icebox. See the
nosedive “pasta” took in the late ’90s? The Atkins diet became popular. During
world wars, we like red meat. These are light applications of a technique that can
have deep reach into our collective psyche.3 Word frequencies can even show
how we perceive abstractions, like the passage of time—something very difficult
to investigate directly. Asking a person what “ten years” means is like asking
him or her to describe a color—you get impressionism where you’re looking for
facts. But looking at writing over time gives us a sense.
The data shows that with each passing year, we’re getting more wrapped up in
the present. For example, written mentions of the year 1850 peaked (in 1851) at
roughly 35 instances for every million words written. Mentions of the year 1900
peaked at 58 per million. Mentions of recent years peak at roughly three times
that. Here are the trajectories of the fifty-year benchmarks in the data set:
Work like this, based on the printed word, helps us understand our larger
culture. Twitter lets us see groups coming together within it. But books and
tweets both are one-to-many forms of communication, and, often, like Major
Ballou’s, our most important words are expressed one-to-one. Users on OkCupid
exchange about 4 million messages a day. Of course, they do so with a special
purpose—dating—but the interface provides no specific prompt and enforces no
limit on what or how much anyone types. Think of it as Gmail for strangers: the
communication on the site is about two people getting to know each other; the
romance comes much later, offline. Outside researchers rarely get to work with
private messages like this—it’s the most sensitive content users generate and
even anonymized and aggregated, message data is rarely allowed out of the
holiest of holies in the database. But my unique position at OkCupid gives us
special access.
First, the site’s decade of history lets us see how technology has altered how
people communicate. OkCupid has records from the pre-smartphone, preTwitter, pre-Instagram days—hell, it was online when Myspace was still a file
storage service. Judging by messaging over all those years, the broad writing
culture is indeed changing, and the change is driven by phones. Apple opened
their app store in mid-2008, and OkCupid, like every major service, quickly
launched an app. The effect on writing was immediate. Users began typing on
keyboards smaller than their palm, and message length has dropped by over twothirds since:
The average message is now just over 100 characters—Twitter-sized, in fact.
And in terms of effect, it seems readers have adapted. The best messages, the
ones that get the highest response rate, are now only 40 to 60 characters long.
By considering only messages of a certain length, and then asking how many
seconds the message took to compose, we can get a sense of how much revision
and effort translates into better results. Below are messages between 150 and
300 characters, plotted against how long they took to write. As you can see,
taking your time helps, up to a point. But the downward bend of the trend lines is
a wingman in numbers, saying don’t overthink it!
Now, the first vertical on the left, the messages that took no more than ten
seconds to write, represents an inordinate amount of the whole and should raise
some eyebrows. It raised mine for sure, and at this point I’m so jaded my face is
frozen—Botox has nothing on ten years working at a dating site. How are so
many people typing messages that long that quickly? The short answer is,
they’re not, and here’s how I know.
Below is a scatter chart of 100,000 messages, with the number of characters
typed plotted against characters actually sent.4 Because there’s a wide range of
counts, running from 1 all the way to almost 10,000, this plot is logarithmic:
I’ve added another diagonal line, and as before, it marks the place where the
two axes are equal—meaning that for the red dots along it, the text matched the
keystrokes that went into it. Essentially, the sender typed what was on his mind
and hit Send, no backspace, no edits. Therefore we know that message A, in the
upper-right corner, was typed more or less in a headlong rush, with almost no
revision. Going back to the logs, I found it took the sender 73 minutes and 41
seconds to hammer out those 5,979 characters of hello—his final message was
about as long as four pages in this book. He did not get a reply. Neither did the
gentleman sender of B, who wins the Raymond Carver award for labor-intensive
brevity. He took 387 keystrokes to get to “Hey.”
But these are the examples at the extremes. The broad gist of the scatter plot
is: as you approach the diagonal, the messages show less revision. Move toward
the bottom right, you get heavy editing, toward the upper left, you
get … physical impossibility. Our chart’s geometry means that as soon as you
cross over the diagonal into the upper half, you’re into people who must’ve
typed fewer characters than their messages actually contained. Who are these
arcane summoners, wringing words from thought alone? They are the cut and
pasters, and they are legion.
We can clarify the graph by making each dot 90 percent transparent. This lets
you see the real density underneath. It’s like we’re taking an X-ray of the data,
and in so doing, we see the bones:
That dense band of dots running just below the diagonal is the writing-fromscratch guys. It’s surprisingly compact. There is, of course, the hard upper
boundary of the line, which separates the from-scratch messages from the pasted
ones, like a border between warring factions. But the band’s lower boundary is
almost as crisp. There appears to be a natural limit to how much effort a person
is willing to put into a message. If you do the arithmetic, it’s 3 characters typed
for every 1 in the finished product.
Above the diagonal are the people who decided that kind of effort was too
much. That diffusion of dots in the upper-left center is all the people who pasted
a templated message and made a few edits to it. Here the logarithmic nature of
the chart can fool you—even just a small amount over that central line means
most of the content in the message is stock. Running up the left side, you see the
dense vertical lines, the ruts. Those are the messages that were “typed” with just
a few keystrokes. There are a lot of them—all told, 20 percent of the sample
registered 5 or fewer keystrokes. These writers settled on something they like or
that works, and they went with it. It’s not spam in the way we normally use that
word—OkCupid is quick to get fake or bot accounts off the site. These are real
people’s attempts at contact, essentially memorized digital pickup lines. Many
are about as lazy and mundane as you’d expect: “Hey you’re cute” or “Wanna
talk?”—just digital equivalents of “Come here often?” But some of the repeated
messages are so idiosyncratic it’s hard to believe they would even apply to
multiple people. Here’s one, presented exactly as typed:
I’m a smoker too. I picked it up when backpacking in May. It used to be a
drinking thing, but now I wake up and fuck, I want a cigarette. I sometimes
wish that I worked in a Mad Men office. Have you seen the Le Corbusier
exhibit at MoMA? It sounds pretty interesting. I just saw a Frank Gehry
(sp?) display last week in Montreal, and how he used computer modelling
to design a crazy house in Ohio.
That’s the whole message—the sender was trying to pick up women who
smoked and were into art. The unstudied “(sp?)” is my favorite flourish. Fortytwo different women got this same message.
Sitewide, the copy-and-paste strategy underperforms from-scratch messaging
by about 25 percent, but in terms of effort-in to results-out it always wins:
measuring by replies received per unit effort, it’s many times more efficient to
just send everyone roughly the same thing than to compose a new message each
time. I’ve told people about guys copying and pasting, and the response is
usually some variation of “That’s so lame.” When I tell them that boilerplate is
75 percent as effective as something original, they’re skeptical—surely almost
everyone sees through the formula. But this last message is an example of a
replicated text that’s impossible to see through, and, in a fraction of the time it
would’ve taken him otherwise, the sender got five replies from exactly the type
of woman he was looking for. And let me tell you something. Nearly every
single thing on my desk, on my person, probably in my entire home, was made
in a factory alongside who knows how many copies. I just fought a crowd to
pick up my lunch, which was a sandwich chosen from a wall of sandwiches.
Templates work. Our social-smoking architecture-loving backpacker is just
doing what people have always done: harnessing technology. In this case his
innovation is using a few keyboard shortcuts to save himself some time.
As we’ve seen, phones and services like Twitter demand their own
adaptations. The eternal here is that writing, like life itself, abides. It changes
form, it replicates in odd ways, it finds unexpected niches … it even, like
anything alive, occasionally stinks. But realize this: we are living through
writing’s Cambrian explosion, not its mass extinction. Language is more varied
than ever before, even if some of it is directly copied from the clipboard—
variety is the preservation of an art, not a threat to it. From the high-flown
language of literary fiction to the simple, even misspelled, status update, through
all this writing runs a common purpose. Whether friend to friend, stranger to
stranger, lover to lover, or author to reader, we use words to connect. And as
long as there is a person bored, excited, enraged, transported, in love, curious, or
missing his home and afraid for his future, he’ll be writing about it.
1 Definition of true ignorance: getting your “what the kids are into” intel from the Securities and Exchange
Commission.
2 Liberman (and I) stripped URLs and the special signs @ and # from the analysis, so these numbers aren’t
artificially boosted by “nonword” material.
3 The data in Google Books accounts for the fact that more books are published now than were published
in, say, the nineteenth century. It samples a set number of books from each year. So though both the charts
here happen to show increased mentions of their subject terms over time, that truly is a function of
increased interest. Not all terms follow that pattern—“God,” for example, has been in steady decline for
decades and is now used only about a third as much in American writing as it was in the early 1800s. The
researchers Jean-Baptiste Michel and Erez Lieberman Aiden coined the term “culturomics” in their paper
“Quantitative Analysis of Culture Using Millions of Digitized Books.” My charts and findings here are
adapted from their work.
4 I captured the characters typed through a script introduced for this chapter.
4.
You Gotta Be the Glue
A major drawback to data from dating sites is that it
tells you next to nothing about people actually going on dates. Once people are
together in person, they don’t need messages or ratings or any of that. It’s an
irony both in the data set and in the job itself—you do it right and the customers
leave. In pairs, no less!
Where they go, of course, is into the real world, into a bar, into daylight, into
the flesh. They depart the easily quantified world of bits and pixels and enter, in
short, each other’s lives. Think about the progression of a young relationship.
Two people meet for the first time in person. Talk, drink, get to know each other.
Next, if there is a next, is the apartments. The unfamiliar number on the door, a
brass handle where yours is steel. The strange but pleasant smell of another
person’s sheets. Shampoos in the shower, used, but new to you. Loganberry:
Okay, why not? Back at your place next time, she opens the fridge, and it’s
just … mustards. Sorry. We’ve all been there in someone’s bedroom, in the den,
amidst mementos of events and people we don’t remember, wondering first at
the tchotchkes themselves and then soon enough at how surprisingly yours
something like the Ponderosa Invitational Swim Meet (third-place cup, 1985)
can become, in spite of the fact—or is it because?—you only know it through
her.
You meet the friends. The best friend. The other best friend. The other other
best friend, like, for real, they’ve known each other forever. Enough drinks, the
right kind of people, they become your friends, too. Acquaintances, coworkers
filter into the picture, some in passing, some on purpose. Finally, maybe, if it’s
really turning into something, come the parents. You relate some fancier version
of your life story, parts of which the two of you can tell together, because you’re
that familiar—step away from the table for a second, and the parents know more
about you than when you left. Settling back into your chair: “M tells me that …”
and it’s the perfect setup for one of your favorite stories. Two lives are merging.
And then, often, and often suddenly, it’s back to the beginning with someone
else.
We’ve had a look so far at the ways two people come together in the first
blush of attraction. I’m not sure a computer will ever capture their path to full
togetherness, but we do have a picture of their lives once they get there. That
pattern of a couple together, the enmeshing of what’s come to be called their
“social graphs,” is now well documented.
I have 384 friends on Facebook, and here they are. I’m the dot in the middle;
my wife, Reshma, is in black at about three o’clock. Everyone’s connections to
everyone else are shown by the gray lines:
Though the groups of my friends are nicely clustered, this plot wasn’t
arranged by hand—my able research assistant, James Dowdell, wrote special
software to create it. The dots come together based on their number of shared
connections. Think of them as little bits of iron dust magnetized by the POWER
OF FRIENDSHIP, and then dropped on a tabletop to settle into place. Even
though I don’t use Facebook for much of anything besides the highly circular
task of accepting Facebook friend requests, you can see all the sides of my life in
there. My very tight-knit set of in-laws, as near to overlapping as the software
would allow, is A; the people I went to high school with are B; my coworkers
are C; my gamer friends, D. You can even read my once and future career as a
musician in the graph. I spent years touring in a band, and those singleton dots
all along the left perimeter are primarily people I met on the road. Their bond to
one another is our music, invisible to algorithms.
Let me expand the graph to include Reshma’s connections as well, to show
the scope of our network as a couple. The connections we share, our mutual
friends, are in dark red.
Though this might seem like a dry abstraction of a couple’s life together, a
mutual plot like this tells you a tremendous amount about the bond between the
two people it’s built around. From just the plot, the image alone, we can
calculate that Reshma and I are much less likely than other couples to break up.
Network analysis, the study of dots and lines just like the patterns above, has
been a science for almost three hundred years, and you can see something of the
rise of data (from trickle to cataclysm) in its progress. The first network problem
was a kind of rustic brainteaser, really an Enlightenment-era urban legend, that it
was impossible to walk through the Prussian city of Königsberg by crossing each
of its seven bridges once and only once. In 1735, Leonhard Euler, as geniuses
will do, came along and reduced what had been a colloquial question of
neighborhoods and footpaths to an abstraction of dots and lines (formally: nodes
and edges), and in doing so, he proved with rigor that the legend was true. He
expressed the town as a network, and a discipline was founded.
Euler’s insight was that because you’re only supposed to cross each bridge
once, to enter a new neighborhood you need a pair of bridges—one to get you
in, another to get you out. So the solution is as simple as looking at the network
plot and asking whether each point along your path, other than your beginning
and end, has an even number of lines (a pair of bridges) attached. In Königsberg,
none of them do, so the problem was solved. That from such homely origins can
come an enduring and flourishing science, one that’s only now finding its full
expression, is, I think, the best possible case for the human spirit.1 Euler’s
concept of nodes and edges, which at first unraveled nothing more than a day’s
walk, has since helped us understand disease and its vectors, trucks and their
routes, genes and their bindings, and of course, people and their relationships.
And in just the last few decades, network theory’s application to these last have
exploded—because the networks themselves have exploded.
Forty years ago, Stanley Milgram was mailing out parcels (kits with
instructions and postage-paid envelopes) to a hundred people in Omaha, working
on his “six degrees of separation,” hoping maybe a few dozen adventuresome
souls would participate. His quaint methods—ingenious though they were—
would give him the famous theory, but not quite its proof. In 2011, the
unprecedented and overwhelming scale of Facebook allowed us to see that he
was indeed right: 99.6 percent of the 721 million accounts at the time were
connected by six steps or fewer.
Today, network theory, working on data sets enabled by technology, shows
how people can find new jobs, sort information from nonsense, and even make
better movies. When they built their headquarters, Pixar famously put the only
bathrooms in the building inside the central atrium to force interdepartmental
small talk, knowing that innovation often comes from the serendipitous collision
of ideas. Theirs was an application of “the strength of weak ties,” a concept
postulated in the 1970s with samples in the dozens, but since amplified on new,
robust network data: it tells us that it’s the people you don’t know very well in
your life who help ideas, especially new ones, spread.2
Another long-held idea in network theory is “embeddedness.” One of its
expressions is the amount of overlap in a pair of social graphs—Reshma’s and
my embeddedness is simply how large the red portion of our graph is compared
with the whole. Research using a variety of sources (e-mail, IM, telephone) has
shown that the more mutual friends two people share, the stronger their
relationship. More connections imply more time together, more common
interests, and more stability. But unlike, say, telephone records, or even e-mail,
online social networks attach rich data to a graph’s edges and nodes (not unlike
how dating sites have taken the timeless ritual of courtship and added age and
beauty as variables to study) and of course Facebook is the richest such network
ever created. The effects of that richness are just being felt.
Social-graph analysis began as, and largely remains, a matter of “who knows
who.” The scope of Facebook data—you can go many degrees deep with
practically no added effort—is starting to turn that on its head. For relationships,
and romantic relationships specifically, this data has recently enabled a new,
powerful measure of how strong a bond between two people is. It turns out your
lives should not just be intertwined but intertwined in a specific way. And, rare
among network analysis metrics, who doesn’t know who is the important
quantity.
Two scientists, Lars Backstrom and Jon Kleinberg, working through 1.3
million couples from Facebook, established the idea in a 2013 paper. Their
measure was based on counting the number of times a person and her spouse
functioned as the bridge between disjointed parts of their network as a couple.
Here’s what I mean: the graph on the left below is a hunky-dory scene, more or
less everybody knows one another; it is very highly embedded. But the stronger
marriage is on the right. There, the couple, A and B, are the sole connectors for
the two cliques around them:
This probably feels a little strange—why would you want your network to be
more fractious but for you and your spouse? But like the best ideas, it plays out
intuitively in real life. For example, going back to my own story, Reshma’s
cousin Sheel is highly embedded in her life. The two of them grew up together,
and he, like she does, has connections to virtually every member of their large
extended family, including many people I don’t even know. They’ve known
each other their entire lives, whereas Reshma and I have been married for only
seven years. Sheel and Reshma’s relationship as a central pair would function
much like my left-hand example above. However, Sheel doesn’t know Reshma’s
coworkers. He doesn’t know the members of Reshma’s dance troupe. He doesn’t
know Reshma’s friends from college. I know them all, and what’s more, I am
the only other person in her life these three distinct groups have in common, at
least directly. For these groups, we embody the ideal on the right. It’s worth
noting that if, for example, Reshma and I worked together, or she didn’t dance,
or we went to the same college, we could not play the role we do in each other’s
networks.
Backstrom and Kleinberg call the level to which a relationship fulfills this
ideal its “dispersion” because it shows how disconnected your graph would be
without you—that is, how utterly your social circle would fly to the winds if you
and your spouse were somehow ripped from the center (by, say, having a second
child). I prefer “assimilation” because I think that better captures the upshot:
assimilated people have a unique role as a couple within their mutual network.
Highly assimilated couples function—the two people together—as the bond
between many otherwise unconnected cliques. They are the special glue in a
given spread of dots, and furthermore, they’re a glue like epoxy: it takes both
ingredients to make the thing hold together.
The power of assimilation comes from the fact that your spouse is one of the
few people (if not the only person) you introduce into the far corners of your life.
She is there at work parties, there at reunions, and there when your gamer friends
come over for that all-day Magic: the Gathering blowout you look forward to all
year. (Or she’s not there, if she can help it, but you get the idea.) Meanwhile,
these coworkers, these classmates, and these gamers, though all densely
intraconnected groups themselves, are unrelated to one another but for you and
your spouse.
And here’s why it matters: For married people on Facebook, their spouse is
the most assimilated member of their network an astounding 75 percent of the
time. And, even more important for assimilation as a metric of relationship
strength, the young couples for whom that’s not the case are 50 percent more
likely to break up. In the most stable relationships, the two people play this
unique role in each other’s lives. Considering alternate graphs of a
nonassimilated couple, it makes a certain sense why—in an overly embedded
one, like the left-hand example before, you and your spouse end up competing
with everyone else for time and attention. There’s too much leveling, no
specialness. Too many girls’ nights. Or in a cliquey network without
assimilation, “leading separate lives” can very quickly become “leading secret
lives,” which might look something like this:
Against assimilation, Backstrom and Kleinberg tested many other ways to
evaluate a relationship, and there was one detail in their paper, presented almost
as an aside, that I found particularly wry. Early on, the best predictor of a
relationship doesn’t depend on the couple’s social graph at all; for the first year
or so of dating, the optimal method is how often they view each other’s profile.
Only over time, as the page views go down and their mutual network fills out,
does assimilation come to dominate the calculus. In other words, the curiosity,
discovery, and (visual) stimulation of falling for someone is eventually replaced
by the graph-theory equivalent of nesting.
There’s this idea in computer science that you should be your own customer—
that you should at least have enough confidence in the website or software
you’re foisting on the world to use it yourself. Just like Jonas Salk injecting
himself with his brand-new polio vaccine, you want to prove what you’re doing
is good. Programmers call it dogfooding, as in “Eat your own dog food,”
because as a group they make bad decisions at mealtimes. At some companies,
dogfooding is mandatory. Have a meeting with Microsoft people, and they’ll roll
up with their Windows phones and Surface tablets, dutiful hounds chewing
tough bits of tendon.
You and I don’t have those kinds of orders from on high here, of course. But I
purposefully led this chapter with my own data because, first, I needed to work
the abstract concepts upon a clear example. But also I wanted to show that, in a
book that picks apart so many other people’s highly personal data, I’m willing to
apply the same analysis to myself.
I offer you the same opportunity. To let you test your own marriage,
partnership, or unhealthy codependent friendship against the principles discussed
in this chapter, I have implemented the Backstrom/Kleinberg algorithm at:
dataclysm.org/relationshiptest
Give it a pair of Facebook credentials, and it will not only depict your mutual
graph and your embeddedness but also rank your most assimilated relationships.
The world has now arrived at a place where we can do something with our own
data—we don’t have to wait for a Milgram, let alone an Euler, to teach us about
ourselves. In the same way a service like Facebook or Twitter exposes our data
to academic scrutiny, it reflects it back at us, for scrutiny of our own. Weak tools
to capture and analyze our own physical activity are already here, and better
ones are not long off. When you see people in middle management dickering
with their Fitbits in the elevator, you know the Quantified Self movement is here
to stay. The above is my very small attempt to add to the possibilities.
If you use my app with someone else, here’s hoping you’re at the top of each
other’s lists, and remember: a little creative defriending can give your
assimilation score the necessary boost. Because self-measurement is all well and
good until some ex-girlfriend comes in ahead of your wife.
1 Evidence against: of the seven bridges so famous in Euler’s time, four have since been destroyed. Two by
bombs and two by a superhighway.
2 The original paper has been cited more than 20,000 times.
5.
There’s No Success Like Failure
There’s a great Tumblr called “Clients from
Hell,
”
where anyone can submit their service-industry horror stories. There
are all kinds of cluelessness and oblivion on display, and new posts go up every
few hours. Here’s a typical submission, from someone doing a photo spread:
CLIENT: Can we have a heading on the photo as well?
DESIGNER: Well, it already has a caption.
CLIENT: If the reader misses the caption, then they will still see the heading.
DESIGNER: It would be quite unusual to have both a heading and a caption on a
photo.
CLIENT: That makes sense. Just put a heading next to the caption, then.
My favorite client quote on the site right now is: “I don’t like the dinosaur in
this graphic. It looks too fake. Use a real photo of a dinosaur instead.” The blog
mostly gets submissions from graphic designers, but Clients from Hell’s
popularity speaks to a universal truth. People hate their customers.
I don’t mean hate on an individual level but, en masse, customers, like any
rabble, are to be feared. Anyone who tells you otherwise, from the cupcake-shop
owner down the street to the CEO in the boardroom, is lying. Part of it is the
“… is always right” thing—nobody likes a person with that much power. But by
far the biggest cause of frustration is that people don’t understand and can’t
articulate what they actually need. As Steve Jobs said, “People don’t know what
they want until you show it to them.” What he didn’t say is that showing them,
especially in tech, means playing a game of Pin the Tail on the Donkey with
several million people shouting advice.
If you are, say, a car company and people don’t like some part of your
product, they mostly tell you indirectly, by not buying it. There’s historically
been no open channel between Ford and the folks who want the cup holders to
be green or who think it would be better if the steering wheel were a square,
because, you know, most turns are 90 degrees. That’s why traditional companies
spend so much on market research—they have to stay way ahead of these kinds
of things, because by the time a company like Ford would naturally hear about a
problem, via Accounts Receivable, it’s way too late.
A website is different: if people have a cockamamie idea, someone at the
company is just an e-mail away. And if people don’t use something, the site
notices immediately. Measurements are tracked in real time, down to the finest
grain, everywhere. Whenever you see something new on your favorite site—
Google, Facebook, LinkedIn, YouTube, or anywhere—and you click it, know
that someone, probably wearing headphones and eating Doritos, just saw a little
counter go up by 1. That’s when the richness of data can drive a person crazy:
one of Google’s best designers, the person who in fact built their visual design
team, Douglas Bowman, eventually quit because the process had become too
microscopic. For one button, the company couldn’t decide between two shades
of blue, so they launched all forty-one shades in between to see which performed
better. Know thyself: It was etched into a footstone of the Temple of Apollo at
Delphi. But like the rest of the best wisdom that time has to offer, it goes right
out the window as soon as anyone turns on a computer.
Not knowing what customers need from a car, or even from a particular
website interface—those are matters for a business school or a design workshop.
It’s when people don’t understand their own hearts that I get interested. People
saying one thing and doing another is pretty much par for the course in social
science, but I had a rare opportunity to see people acting in two contradictory
ways. And it all happened because I didn’t know what they wanted either.
On January 15, 2013, OkCupid declared “Love Is Blind Day” and removed
everyone’s profile photos from the site for a few hours. The idea was to do
something different and get a little attention for a new service we were launching
at the same time. The programmers “flipped the switch” at nine a.m.:
It was a bona fide pit of despair—rare in the wild! The new service OkCupid
was trying to promote was a mobile app called Crazy Blind Date. With a couple
taps on the screen, it would pair you with a person and select a place nearby and
a time in the near future for the two of you to meet. The app provided an
interface to let both parties confirm, but there was no way for anyone to directly
communicate before the date. The only information it gave you about the other
person was a first name and a scrambled thumbnail, like the one below. You
were just supposed to show up and hope for the best.
a CBD-style scramble of a stock photo
You’ve probably already noticed that I’m speaking of Crazy Blind Date in the
past tense. Even after a quarter million downloads, it failed, because in the end
people insist on seeing what they’re getting into. The app was one of those ideas
that looks great on a whiteboard and miserable in the full color of creation—it
was like one long “Love Is Blind Day,” and with no way to flip the switch back
to normal. A few months after launch, we shut the service down, but before
Crazy Blind Date went off to the great app store in the sky (little-known fact:
there are no bugs in heaven, just sweet features), about 10,000 people used it to
share a beer or a cup of coffee with someone they’d never seen or spoken to
before.
From these intrepid few, the app bequeathed the world a rare data set. Crazy
Blind Date recorded not only the fact that dater A and dater B met in person but
also their opinions of each other. After each completed date, like a nosy
roommate, the app asked how it went. Because most of the users also had
OkCupid accounts, we were able to cross-reference this data with all kinds of
demographic details. We suddenly had in-person records to combine with our
massive collection of digital interactions. When you merge the two sources you
find something remarkable: the two people’s looks had almost no effect on
whether they had a good time. No matter which person was better-looking or by
how much—even in cases where one blind-dater was a knockout and the other
rather homely—the percent of people giving the dates a positive rating was
constant. Attractiveness didn’t matter. This data, from real dates, turned
everything I’d seen in ten years of running a dating site on its head.
Here are the numbers for men. I’ve expressed attractiveness below as the
relative difference in a couple’s individual ratings, rather than as absolutes. I did
this to capture the fact that a person’s happiness at finding himself across the
table from, say, a “6” is highly dependent on his own looks. If he’s a “1,” he
might be thrilled with that arrangement—it means he’s dating up. A “10” would
feel differently. I’ve included the counts of dates as the bars to show that the
balance in attractiveness between the men and women going on the dates was
about what you’d expect if they were randomly paired. There was no evidence of
people gaming the system by, say, somehow unscrambling the pictures
beforehand or showing up to the date venue and then leaving on the sly when
their blind date arrived and didn’t pass muster. The satisfaction numbers (for
males) are the percentages in red:
And following is the same data for women:
Through both Crazy Blind Date data sets, people just didn’t seem to care that
much about the other person’s physical appearance. Women had a good time 75
percent of the time, men 85 percent. The rest of the variation is basically noise.
That indifference to looks is just about the opposite of what you see in the
OkCupid data. For example, I’ve plotted the in-person satisfaction data above
(the numbers in red) alongside those same women’s reply rates to messages
online. To make it easier to compare them, the lines show change against the
average of their respective quantities:
The male comparison chart is very similar to this one, and, to be clear, the
data underpinning the two lines above is from the same set of people. The black
line is their OkCupid experience, the red from Crazy Blind Date. In short, people
appear to be heavily preselecting online for something that, once they sit down
in person, doesn’t seem important to them.
That kind of superficial preselection is everywhere. In fact, there’s a lot of
money to be made off it. You know what the difference between Tylenol and
Kroger’s store-brand acetaminophen is? The box. Unless you take medicine like
a king snake and plan to just swallow the package whole, there’s really no
reason to pay twice as much for the “name” molecules, whose properties are
determined by immutable chemical law. And yet, I have a big red Tylenol bottle
on my dresser.
We of course pay the most attention to labels when they’re attached to people.
In terms of superficial compatibility, self-described Democrats and Republicans
get along the least of all major groups on OkCupid—worse even than Protestants
and Atheists. I know this through the many match questions the site asks: they
cover pretty much everything, and the average user answers about three hundred
of them. The site lets you decide the importance of each question you answer,
and you can pinpoint the answers that you would (and would not) accept from a
potential match. Despite all this control, in the political case, the system breaks
down. When you look beyond the labels, at who actually messages whom, and
who replies (and therefore who ends up going on actual dates), it’s caring about
politics, one way or the other, that is actually more important to mutual
compatibility than the details of any particular belief. We confirmed this in a
summer-long experiment in 2011.
People tend to run wild with those match questions, marking all kinds of stuff
as “mandatory,” in essence putting a checklist to the world: I’m looking for a
dog-loving, agnostic, nonsmoking liberal who’s never had kids—and who’s
good in bed, of course. But very humble questions like Do you like scary
movies? and Have you ever traveled alone to another country? have amazing
predictive power. If you’re ever stumped on what to ask someone on a first date,
try those. In about three-quarters of the long-term couples OkCupid has ever
brought together, both people have answered them the same way, either both
“yes” or both “no.” People tend to overemphasize the big, splashy things: faith,
politics, and certainly looks, but they don’t matter nearly as much as everyone
thinks. Sometimes they don’t matter at all.
Fiasco though it was, Love Is Blind Day gave us a visceral example of what
people do in the absence of information. In hiding pictures but changing nothing
else, we created a real-time experiment to set against the site’s usual activity. For
seven hours our users acted without the very thing our previous data had
indicated was the single most important piece of knowledge OkCupid could
offer: what everyone else looked like.
Some of the upshot was predictable. People sent messages without the typical
biases, or racial and attractiveness skews. What a user couldn’t see, he couldn’t
judge. But of the 30,333 messages sent blindly, eventually 8,912 got replies, a
rate about 40 percent higher than usual. And in the dark, for those who were
there, something astounding happened. Twenty-four percent of the pairs of
people talking when the photos were hidden had exchanged contact info before
pictures were turned back on. That was in only the seven-hour window of Love
Is Blind Day. The expected number in that amount of time is barely half that. So
not only were people writing messages that were far more likely to get replies,
they were giving out phone numbers and e-mail addresses at a higher rate—to
people they’d never even seen.
For the couples who began talking and were still getting to know each other
when we restored photos at four p.m., however, the day had a reverse effect. The
two people had been in the dark, then suddenly the lights came on, and, in the
data, you can actually see them spook. Threads straddling the moment we
flipped the switch lasted an average of 4.4 more messages. When you compare
them against a control data set, they should’ve lasted 5.6. Eventual contact-info
exchanges in those “lights on” threads were down by a similar amount.
Dating sites are designed to give people the tools and the information to get
whatever they want out of being single—casual sex, a few fun dates, a partner, a
marriage … anything. Stuff like height, political views, photos, essays, all of it is
right there, easily sortable, easily searchable. It’s there to help people make
judgments and fulfill their desires, and as fascinating as those judgments and
desires may be to pick apart, there’s a side of it that I think does love a
disservice. People make choices from the information we provide because they
can, not because they necessarily should.
I can’t help think of the many people getting turned down because of some
perceived “deal-breaker” that actually no one cares about and wonder if the
Internet has changed romance in the way it’s changed so much else—and for the
same reason. If I may channel my inner anti-Jagger: Online, you can always get
what you want. But what you need, that’s a much harder thing to find.

6. The Confounding Factor
7. The Beauty Myth in Apotheosis
8. It’s What’s Inside That Counts
9. Days of Rage
6.
The Confounding Factor
If you stand on the southwest corner of Fifty-Eighth and
Fifth with a clipboard and do a little people-watching, you can very quickly
conclude that most New Yorkers are beautiful, thin, and above all, rich. Every
thread, every grommet, every crease shines with money. Of course, many New
Yorkers are rich, but that’s not the whole story here. You’re standing outside
Bergdorf Goodman, and that’s a confounding factor.
This is a technical term for something you haven’t accounted for in your
analysis but that nonetheless affects its results. Making sure you’re not perched
in some bitwise version of the Upper East Side is one of the most time- and
thought-consuming parts of working with digital data. When you have
seemingly every variable and every possibility available for analysis and
speculation, your research is free to travel wherever your curiosity leads. But
true to the cliché, that freedom requires eternal vigilance.
And here’s where I have an admission to make. So far in these pages,
wherever you’ve seen the data of a person-to-person opinion, in the votes, in the
date results from Crazy Blind Date, the charts, the tables—in every ratio, in
every total—whenever one user was judging another, both people involved were
white. I had to make it that way, because when you’re looking at how two
American strangers behave in a romantic context, race is the ultimate
confounding factor. And to make sure whatever I wanted to say about attraction
or sex spoke to those ideas alone, I needed to cut it from the discussion.
As an American, the reflex to sweep race under the rug is inborn, so in a way,
though the numbers forced my hand, I was just doing what came naturally. And
even apart from our nation’s peculiar relationship with the topic, a long history
of tokenism and sorry pseudoscience makes any quantitative analysis of race
especially fraught. That’s not to say we don’t have good numbers. There are
plenty of them, of a certain type—if my preferred data is person-to-person, then
I think of this other as person-to-thing: one group or another versus
unemployment rates, the SAT, the criminal justice system, cancer … As much as
research like this has helped us pinpoint and (occasionally) address inequality,
there’s something incomplete about it. You lose the human who is doing (or not
doing) the hiring, the teaching, the police work, the preventative care; you lose
the people who created the outcomes that all these studies purport to measure. So
what you end up with is conclusions like this: Black Defendants Are at Least 30
Percent More Likely to Be Imprisoned Than White Defendants for the Same
Crime. The headline’s passive voice says it all. Who’s miscarrying the justice
here? Syntactically, no one. Practically, I have a good guess. But it is a rare
study indeed that looks beyond the institutions, to the fundamental “us versus
them” binary of race relations.
Behind every bit in my data, there are two people, the actor and the acted
upon, and the fact that we can see each as equals in the process is new. If there is
a “-clysm” part of the whole data thing, if this book’s title isn’t more than just a
semi-clever pun or accident of the alphabet—then this is it. It allows us to see
the full human experience at once, not just whatever side we happen to be
paying attention to at a given time.
Before the advent of data like ours, one of the most quantified arenas in public
life was sports. There you have real-time numbers on every conceivable
interaction, and you have the data on an individual level, to be sliced and
recombined at will. Perhaps it’s surprising, then, that sports is where the
discussion of race is least analytic. The “black quarterback” controversy that
stretched for the first ten years or so of this millennium is the perfect example.
For years there was a regular news cycle: an African American quarterback
would go early in the draft or start a high-profile game, and someone would
inevitably imply that blacks can’t succeed at the position in the NFL. The usual
reason given was that they lacked the intelligence. There would be backlash,
discussion, and plenty of argument that this was nothing more than meanspirited stereotyping. But amidst all the commentary and outcry, and outcry
against the outcry, in the 97,000 results that Google returns for “black
quarterback,” I found only one article that actually calculates the quarterback
ratings of blacks and whites, which turn out to be the same down to the second
decimal: 81.55. In a genre so stats-obsessed, where platoons of number
crunchers calculate Johnny Placekicker’s 54 percent success rate on field goal
attempts over 50 yards in road games decided by 7 points or less against AFC
opponents, you’d think that statistically comparing black and white quarterbacks
would’ve been everyone’s first instinct. Instead, there was, and generally is
around race, an eerie numerical silence. You find in its place rhetoric and
appeals to anecdote. But a “debate” done in this style just leaves everyone
believing they’re right, when, in fact, for all the words expended, a single
number—81.55—can clearly show that one side is wrong. The article that did
the rating calculation had 0 tweets and 0 Facebook likes, by the way, and it
wasn’t posted on some obscure blog; it appeared on The Big Lead, which is
owned by USA Today. You often get the feeling that people just don’t want to
know.
Where in situations like this we might seem to lack the will to examine race
through a statistical lens, in many other arenas we have simply lacked the data.
Most aspects of life haven’t been as obsessively quantified as football. That is
changing rapidly.
On OkCupid, one of the easiest ways to compare a black person and a white
person (or any two people of any race) is to look at their “match percentage.”
That’s the site’s term for compatibility. It asks users a bunch of questions, they
give answers, and an algorithm predicts how well any two of them would get
along over, say, a beer or dinner. Unlike other features on OkCupid, there is no
visual component to match percentage. The number between two people only
reflects what you might call their inner selves—everything about what they
believe, need, and want, even what they think is funny, but nothing about what
they look like. Judging by just this compatibility measure, the four largest racial
groups on OkCupid—Asian, black, Latino, and white—all get along about the
same.1 In fact, race has less effect on match percentage than religion, politics, or
education. Among the details that users believe are important, the closest
comparison to race is Zodiac sign, which has no effect at all. To a computer not
acculturated to the categories, “Asian” and “black” and “white” could just as
easily be “Aries” and “Virgo” and “Capricorn.”
But this racial neutrality is only in theory; things change once the users’ own
opinions, and not just the color-blind workings of an algorithm, come into play.
Given the full profile, with the photo dominating the page, this is how
OkCupid’s users rate each other by race:
I’ve given the raw data above, unadorned, because by now you’re at least a
little familiar with OkCupid’s 1- to 5-star system. But to make the trends easier
to see, I’m going to take that same matrix and “normalize” each row. In the table
below, each entry is the percentage difference (+/-) from the average (the
“normal”) in the row. It’s the same information, just phrased a bit differently.
Think of the normalized number as the men’s relative preference for women. For
example, as you can see, Asian men think Asian women are 18 percent betterlooking than the average, while black men think they’re just 2 percent better.
And so on:
I’ll soon move beyond OkCupid, and when I present similar matrices later, I’ll
go directly to the normalized scores. But for now, the two essential patterns of
male-to-female attraction are plain: men tend to like women of their own race.
Far more than that, though, they don’t like black women. Message data is highly
correlated with these ratings, so they follow the pattern as well.2
Just to show that these voting trends aren’t being thrown off by some obscure
statistical artifact, I’ve put the raw per capita vote numbers in what’s called a
box plot—it tells you where the bulk of a data set lies. You see below that the
central mass of black women is rated almost entirely below the other three
ethnicities’, and the black women’s upper extreme is about at the midline of the
other three:
Mathematically, this is a complete discount—being black basically costs you
about three-quarters of a star in your rating, even if you’re at the top. Further,
when you do this analysis in reverse, and look at the people actually casting the
votes, you see a similar wholesale pattern. The broad majority of non-black men
apply that three-quarters reduction to black women. There is no cadre of racists
single-handedly bringing everything down.
However startling this may be, it only reflects one data set, the thoughts of one
group of people. So here’s a good place to pause for a second and answer a
question you might have been asking earlier, given how much I’ve relied on
OkCupid’s data so far in this book: Who are these people?
In the most superficial way, OkCupid’s members reflect the general
composition of Internet users, with of course the caveat that (almost) everyone
on the site is single. The site’s users are younger than the national average
(OkCupid’s median age is twenty-nine), and they tend to be less religious. The
racial composition is about what you’d expect. Here are our numbers compared
with the generic “American Internet User” breakout from Quantcast, the major
online audience measurement firm—it’s like Nielsen for the net.
Going one demographic level deeper, OkCupid users are, if anything, more
urban, more educated, and more progressive than the nation at large. The site’s
biggest markets by far are places like New York, San Francisco, Los Angeles,
Boston, and Seattle. Eighty-five percent of the users have gone to college. Selfdescribed liberals outnumber self-described conservatives more than two to one.
There is a broad, site-wide ethos of open-mindedness. And an unintentionally
hilarious 84 percent of users answer this match question …
Would you consider dating someone who has vocalized a strong negative
bias toward a certain race of people?
in the absolute negative (choosing “No” over “Yes” and “It Depends”). In light
of the previous data, that means 84 percent of people on OkCupid would not
consider dating someone on OkCupid.
Essentially anything that, in theory, would make a group of people “less
racist,” that’s what OkCupid users are. I point this out to people, who, like me,
lead nice lives in large, diverse cities; who think of their opinions and tastes as
nothing if not enlightened; who unwind at night with a glass of wine and a
Facebook dose or two of progressive righteousness: When I show here that black
women and later, black men, get short shrift, and that adding whiteness to a
user’s identity makes him or her more attractive, I’m not describing some Ozark
fever dream. I’m describing our world, mine and yours. If you’re reading a
popular science book about Big Data and all its portents, rest assured the data in
it is you.
But look one more time at the match question above, which was written by
one of OkCupid’s users and has been answered close to two million times:
“vocalized” is an odd word. Get rid of it, and it still more or less reads “Would
you date a racist?,” which I once assumed was the question’s real intent. The
writer, however, understood the subtleties of the data set before I did. On a
dating site you can act on impulses that you might otherwise keep quiet. On
some level, the users come to judge and be judged by others, and each person
joins the site free of the context of their everyday life. The site doesn’t connect
you to your family. Nothing gets posted to your friends’ timelines. The game is:
it shows you people, and you like them or you don’t; you talk to them or you
don’t. There’s nothing else to it. In a digital world that’s otherwise compulsively
networked, there’s an old-school solitude to online dating. Your experience is
just you and the people you choose to be with; and what you do is secret. Often
the very fact that you have an account—let alone what you do with it—is
unknown to your friends. So people can act on attitudes and desires relatively
free from social pressure.
In the layperson’s mind, Facebook, “the social network,” is the sine qua non
of online data sources. And it’s easy to see why: Facebook is huge and
pervasive, and a sample of their users is pretty much a sample of people
worldwide who have Internet access—in other words, you can easily get a
representative corpus for whatever you want. And they have such robust and
diverse data: they know who you went to high school with, what song you just
listened to on Spotify, where your parents live, and so on.
But as often as it is an asset, that richness can be a liability. You rarely meet a
stranger on Facebook. The site is, by design, people you already know and
whom you’ve already made up your mind about—they’re your friends, after all.
Facebook’s data on race is the embodiment of the “But I have black friends”
solipsism you often hear. How you treat your friends is, by definition, the
exception to how you treat the rest of humanity. And you and your friends’
relationships were formed outside of the network first.
Moreover, people become inhibited when their friends are watching. This
fishbowl aspect is why the first step of most dating apps on Facebook is to get
you off Facebook—your existence there is fully chaperoned. Long ago, we tried
“social” features on OkCupid, and they bombed, as did similar features when
Match.com gave them a go. For whatever reason, people don’t want their
network along for online dating. The desire for solitude comes from the same
place, I imagine, as the claustrophobia that would grip most of us if, on a
promising first date at some restaurant, two old friends posted up at a nearby
table. This is to take nothing away from the business or the community
Facebook has created, but the “real life” relationships that both undergird and
overarch the site give a different power to their data. When you want to look at
something like race, where, at least among decent people, there’s pressure to
behave a certain way in public, dating sites provide a uniquely powerful data set:
everyone’s a stranger, alone, and there to tell you who they like and who they
don’t.3
So then let’s put OkCupid’s data up against data from other dating sites and
see what shakes out. Looking at numbers made by other users, acting through
other interfaces, gives us a much better sense of the real pattern. And that’s what
we see below—this is data from OkCupid, DateHookup, and Match.com, sites
that together signed up about 20 million Americans last year alone, presented
side-by-side. In the particulars, the matrices vary—remember, these values
reflect actions produced by different people using different software—but
cutting through that difference is the same broad pattern. In terms of the
“direction” of feeling, like or dislike, these matrices are very nearly identical:
Match.com, you probably know. It’s been the most popular dating site in the
United States for almost two decades. They buy tons of advertising on national
television and, as a result, have exactly the broad “all-American” demographics
you’d expect. DateHookup is a free site of several million members that is very
popular among casual daters; its user base is just under 20 percent black and 13
percent Latino. It’s the most diverse of the three sites considered here. I think of
it as the Atlanta or the Houston to OkCupid’s Portland and Match’s Dallas. But
as you see, across all three sites, for men rating women, you get the same pattern
wherever you go.
The votes in the other direction, of women rating men, aren’t quite as uniform
from site to site, though they’re still very similar:
These matrices show two negative trends, and two positive. Blacks are again
unappreciated by non-black users, but Asian men have joined them in the red.
On the positive side, women clearly prefer men of their own race—they’re more
“race-loyal” than men—but they also express a clear, secondary, preference for
white men.
Another way to dig into racial hierarchies is open to us on OkCupid, and it
reinforces this “white preference.” Because the users are able to select more than
one ethnic identity, we can study racial blends in an almost laboratory-like way.
For example, we have men who check “Asian” as their ethnicity. We also have
men who check both “Asian” and “white.” Comparing the two groups gives us
some sense of what adding “whiteness” gets a person. It turns out: quite a bit.
When you add white, ratings go up, across the board. I’ve just spilled out the
complete data here. It’s a big, messy table, but it’s worth exploring.
Down the right-hand column you see the improvement in scores created by
whiteness in a person’s racial makeup. The biggest takeaway is that the racial
discount applied to black men and women and Asian men in the tables above is
significantly undone here. It’s the reverse of the old “one-drop” rule.
Unfortunately, there aren’t enough people who select “black” and “Latino” or
“Asian” and “black” to fully flesh out this alchemy, but it’s an intriguing
glimpse at how we view the ethnic spectrum:

Now, this is all taken from ratings on a dating website, but dating data is
essentially data of the first impression, of the first blush—the users need to get to
know each other, at least a little, before they’re going to want to kiss—and it’s in
that same basic spirit that any pair of people come together: Well, what am I
looking at? Who do I see? The data measures the frisson of meeting someone
new: that burst of judgment and instinct and chemistry that determines whether
you like a person or not, before you even really know much about them. Here
are a few OkCupid users putting it in their own words:
Then one day, I think I was looking through my daily matches and there he
was. I instantly clicked on his profile … something about him, just made
me smile.
—Bella, on Patrick
Well, it all began when one day I am looking through my matches and see
this girl that I found attractive from first glance.
—Dan, on Jenn
But if there is love at first sight, there is dislike at first sight too, right? And is
it not that same frisson of attraction, but in reverse, when someone flinches,
however unconsciously, from a stranger? Here, again, someone in his own
words:
There are very few African American men who haven’t had the experience
of walking across the street and hearing the locks click on the doors of cars.
That happens to me.… There are very few African Americans who haven’t
had the experience of getting on an elevator and a woman clutching her
purse nervously and holding her breath until she had the chance to get off.
That happens often.
—Barack Obama, July 19, 2013
These flashes of intuition at the core of the data—extrapolations from just the
smallest amount of information—pertain not just in romance, but in picking who
you rent your apartment to, in deciding to approve a loan or not, and, surely, in
police work, where there’s often no time for anything but a flash. Even in more
deliberate situations, the first impression plays the heavy. One paper asked: “Are
Emily and Greg More Employable Than Lakisha and Jamal?” and got a
resounding “Yes” from our nation’s HR professionals. The scientists sent
identical résumés, some with “black-sounding” names at the top and some with
“white-sounding” ones, and found that the latter received 50 percent more
responses, no matter the position or industry. And companies that say they’re
“Equal Opportunity Employers” discriminate as much as anyone else.
That kind of irony gets to why big studies are important, but small person-toperson measurements are essential: when you read findings like the one above,
and see that Jamal doesn’t get the job, it’s easy to shake your head at the few
racist hiring managers who’ve tilted the odds against him. But the data we see in
this chapter shows racism isn’t a problem of outliers. It is pervasive. We’ve seen
the same patterns repeated on three different sites, with different users and
different experiences: men, women, free, subscription-only, casual, serious,
“urban” demographics, and more “mainstream.” All told, the research set
represents a large chunk of the young adults in this country, and the data
uniformly shows non-blacks discount African American profiles. It’s not a
problem caused by a small cluster of “ugly” black users or by a small group of
unreformed racists throwing off an otherwise regular pattern.
It is no longer socially acceptable to be openly racist. In response to that
pressure, there is some portion of the public who have therefore slunk away: if I
can’t shout hate at some schoolchildren anymore, well, fine, I’ll just shout it at
the TV. This is not the typical American. Most of us—almost all, in fact—
recognize that racism is wrong. But it is still implicit in many of the decisions we
make.4 Psychologists have a name for the interior patterns of belief that help a
person organize information as he encounters it: schema. And our schema is still
out of step with how most of us know the world should be. By hundreds of
small, everyday actions, none of them made with racist intent or feeling, we
reflect a broader culture that is, in fact, racist. As we’ve seen, the pattern is so
woven-in that relatively recent additions to our society, Asians and Latinos, have
adopted it, too.
When it comes to these patterns, the individuals are, in a way, blameless. That
black people get three-quarters the affection on dating sites is practically an
accident. I can’t fault someone for not wanting to go on a date with someone
else. There’s rarely any malice in that decision. Judgments like votes are made in
an instant, and are such small, seemingly meaningless, things. You browse
around and maybe one face in twelve is black. And looking at that person your
action at that time could go in any direction, just as it could if you were looking
at a white user; you’re in the flow. And so what if you don’t like one particular
person at one particular moment? It is everyone’s right to think what they want
about any individual—in fact, seeing each person as an individual in the first
place, and not as a category, is a huge step in the right direction. It’s just that the
patterns in aggregate show that the dice, overall, are still loaded. Actually, a
better metaphor from the same general category: they show that the house is still
taking a rake—it’s not the dealer, it’s not the hand, it’s not even the play, it’s the
rules of the game that make certain groups of people lose and others win.
Sociology professor Osagie K. Obasogie recently produced some ingenious
research—he interviewed people blind from birth and found the same attitudes
about race as in the sighted world. His sample was relatively small—just 106
individuals, but he found my OkCupid data in the flesh. He cites numerous
examples of a young blind person being happy on a date until some “tell”—
usually the feel of the hair but occasionally a whisper from a stranger—revealed
that the other person was black. The date was then over.
Obasogie asserts that blind people’s attitudes on race reflect a lifetime of
cultural absorption, as opposed to any visual reality. From his data, it seems
impossible to argue otherwise. Moreover, he observed that sex is the locus of the
sharpest discord between what we’re looking at and what our culture tells us we
see. As he puts it to the Boston Globe, he was struck by the vigilance with
which, even among his blind subjects, “racial boundaries get patrolled, primarily
in the realm of dating.” To take his metaphor one step further, a patrol protects
the interior, and here dating is just the frontier of a vast cultural mass that will
take decades to rearrange.
Anyhow, I’m well aware of the long and embarrassing history of “science” by
white researchers conducted to “prove” the scientist’s belief that white people
are better. And I’m equally well aware of how data showing that, just for
example, “women find white men attractive” can come across. It is not my claim
that white men are unusually good-looking. Nor am I claiming that the data
“proves” black people aren’t attractive. In fact, OkCupid’s patterns change in
places outside the United States. In the UK, the site’s black members get 98.9
percent of the messages white members do. In Japan, 97.8 percent. In Canada,
90 percent. Many of the black users in the former two countries, especially
Japan, are Americans abroad.
Sex sometimes has nothing to do with bone structure and muscle and flesh—
the flaws and boons of which all races share in equal amounts. There is culture
there too, and expectation, and conditioning. That’s what this data shows, and
because it’s person-to-person, and collected in fine detail, it can show it in a way
that no other research can.
I was an exchange student in Japan for a summer in high school, and the
agency officials in my host town, Utsunomiya, would occasionally collect me
and the other Americans to visit a school or a factory nearby. The goal was as
much for us to see the country as for it to see us. This was the early ’90s, preInternet, and Japan, not China, was still our big economic rival. There was
tension; they had bought Rockefeller Center a few years before; the yen was
threatening the dollar. The name of my exchange program captured the timbre of
the visit in three words: Youth for Understanding.
The name notwithstanding, I found the culture baffling. I remember even the
characters’ names in Street Fighter II were all wrong; Vega was called Balrog
and Balrog was M. Bison.… I was like, This is madness. But they did have
American television; Baywatch would soon be the number one show in the
country. At one school they bundled us off to, we had to get up and say a few
words in front of the student assembly. I rose from the floor to the podium, said
something dumb, and stepped down. The next person due up was the only
blonde in our little troupe, and as she stood, and I’ll never forget it, there was an
audible gasp. The person standing there was just a regular girl—we were sixteen
and all lumpy and horrid—but a shudder went through the crowd as if Pamela
Anderson were there in the flesh.
Many people have taken that shudder at face value. And for decades,
phrenologists, racialists, and quacks have jumped through hoops to give that
essentially cultural response a biological (and therefore immutable) basis. Nell
Irvin Painter’s book The History of White People gives an excellent overview of
“race science,” and in the course of it she offers up a quote from an
Enlightenment-era text on the wonders of the “Caucasian” race, written,
naturally, by a white man:
The blood of Georgia is the best of the East, and perhaps in the world. I
have not observed a single ugly face in that country, in either sex; but I
have seen angelical ones. Nature has there lavished upon the women
beauties which are not to be seen elsewhere … it would be impossible to
point to more charming visages, or better figures, than those of the
Georgians.
Johann Blumenbach was the writer here; he developed his racial theories by
collecting and comparing human skulls. Scholarship, perhaps, has progressed.
The subconscious is another story.
1 Of course, not every person on OkCupid puts themselves in one of these neat categories. However, to
simplify and focus the discussion, we’ll limit our analysis to users who have selected one of the four.
2 Black women get roughly 75 percent of the number of first messages that other women do. Their
messages are replied to about 75 percent as often.
3 Now, of course, dating sites are far from a perfect general source. As we both know, almost every user is
single, and that has consequences. Using our data, if I were to sit here and research, say, spending habits,
and thus conclude that the average American man spends all his disposable income on restaurants and
movie tickets, I’d be making a fool of myself. A claim like this, oblivious to the special nature of my
source, would be absurd.
4 To be clear, “we” isn’t rhetorical. It means me, too.
7.
The Beauty Myth in Apotheosis
I work in a universe where people identify themselves along
almost every conceivable axis—as smokers and non-; as Christians and atheists;
as nerds or geeks, or maybe dorks; to say nothing of black or white or Asian or
gay or straight, or neither, or both. Mankind is tribes within tribes. Or, putting it
more beautifully, like the Korean proverb: “Over the mountains, mountains.”
That’s the ruggedness of their peninsula and the endless difficulty of our
fractured human terrain.
Running a dating site you become aware of a subdivision that on the one hand
seems frivolous but on the other is as inborn as a person’s race or sexuality, and
like those latter traits it’s often resistant to direct analysis. On OkCupid—as on
Match, as on Tinder—a prime divide, perhaps the deepest, is between the
beautiful and the rest. These are our haves and have-nots, our rich, our poor, and
when it comes to sexual attention, the haves reap the benefit of their inheritance
just as surely as any heir, while the have-nots largely go without. Not unlike
race, beauty is a card you’re dealt, and it has huge repercussions.
Below I’ve plotted new messages received per week, by the recipient’s
physical attractiveness:
The sharp rise out at the right smashes down the rest of the curve, so its true
nature is a bit obscured, but from the lowest percentile up, this is roughly an
exponential function. That is, it obeys the same math seismologists use to
measure the energy released by earthquakes: beauty operates on a Richter scale.
In terms of its effect, there is little noticeable difference between, say, a 1.0 and
2.0—these cause tremors that vary only in degree of imperceptibility. But at the
high end, a small difference has cataclysmic impact. A 9.0 is intense, but a 10.0
can rupture the world. Or launch a thousand ships.
What you definitely can’t see in the chart above, because I aggregated the data
to obscure it, is that men and women experience beauty unequally. Here is that
OkCupid message density, split out by gender, with the aggregates as the dotted
line in the middle.
It’s hard for me to convey how much attention the upper-right corner of this
curve entails, short of tracking you down and screaming in your face about my
hobbies. Especially in larger cities, where the message flow is 50 percent higher
than even what you see above, a woman at the top of the scale has something
like a term paper’s worth of hey-what’s-up-do-you-like-motorcycles-because-Ilike-motorcycles waiting for her every time she comes to the site. A dudeclysm,
if you will. However, neither beauty’s effects, nor the male/female split, are
confined to the sexual realm.
Here is data for interview requests on Shiftgig, a job-search site for hourly and
service workers:1
And for friend counts on Facebook:
Success and beauty are correlated for both sexes, but you can see that the
slope of the red line is always steeper. On Facebook, every percentile of
attractiveness gives a man two new friends. It gives a woman three. On Shiftgig,
the curves aren’t even comparable in this way. The female curve is exponential
and the male is linear. Moreover, they hold whether the hiring manager, the
person doing the interviewing, is a man or a woman. In either case, the male
candidates’ curves are a flat line—a man’s looks have no effect on his prospects
—and the female graphs are exponential. So these women are treated as if
they’re on OkCupid, even though they’re applying for a job. Male HR reps
weigh the female applicants’ beauty as they would in a romantic setting—which
is either depressing or very, very exciting, depending on whether you’re a lawyer
with a litigation practice. And female employers view it through the same
(seemingly sexualized) lens, despite there (typically) being no romantic intent.
It is hardly fresh intellectual ground that beauty matters, and that it matters
more for women. For example, a foundational paper of social psychology is
called “What Is Beautiful Is Good.” It was the first in a now long line of research
to establish that good-looking people are seen as more intelligent, more
competent, and more trustworthy than the rest of us. More attractive people get
better jobs. They are also acquitted more often in court, and, failing that, they get
lighter sentences. As Robert Sapolsky notes in the Wall Street Journal, two
Duke neuropsychologists are working on why: “The medial orbitofrontal cortex
of the brain is involved in rating both the beauty of a face and the goodness of a
behavior, and the level of activity in that region during one of those tasks
predicts the level during the other. In other words, the brain … assumes that
cheekbones tell you something about minds and hearts.” On a neurological level,
the brain registers that ping of sexual attraction—Ooh, she’s hot—and
everything else seems to be splash damage.
To my second point, that beauty affects women in particular, Naomi Wolf’s
bestseller The Beauty Myth showed that better than I ever could. In short, my
raw findings here are not new. What is new is our ability to test ideas,
established ones, famous ones even, against the atomized actions of millions.
That granularity gives strength and nuance to previous work, and even suggests
ways to build on it.
The paper “What Is Beautiful” was based on a research sample of only 60
subjects—barely adequate to prove the effect, let alone its many facets.2 But
now we can go from “What Is Beautiful Is Good” to asking “How Good?” and
in what contexts. In sex, beauty is very good. In friendship, it’s only somewhat
good, and when you’re looking for a job, the effect really depends on your
gender. As for Wolf’s seminal work, we can confirm the truth behind her broad
observation that “today’s woman has become her ‘beauty’ ”—three robust
research sets agree that the correlation is strong. And, better, we can extend
some of her most cogent arguments about beauty being a means of social
control. Think about how the Shiftgig data changes our understanding of
women’s perceived workplace performance. They are evidently being sought out
(and exponentially so) for a trait that has nothing to do with their ability to do a
job well. Meanwhile, men have no such selection imposed. It is therefore simple
probability that women’s failure rate, as a whole, will be higher. And, crucially,
the criteria are to blame, not the people. Imagine if men, no matter the job, were
hired for their physical strength. You would, by design, end up with strong men
facing challenges that strength has nothing to do with. In the same way, to hire
women based on their looks is to (statistically) guarantee poor performance. It’s
either that or you limit their opportunities. Thus Ms. Wolf: “The beauty myth is
always actually prescribing behavior and not appearance.” She was speaking
primarily in a sexual context, but here, we see how it plays out, with
mathematical equivalence, in the workplace.
As I’ve mentioned before, I have a young daughter, and in our rare downtime,
Reshma and I will speculate about her and her life and where it might lead. All
parents do this—give them a quiet moment and it’s inevitable, just like two
drunks in a bar will always argue. Every family must have their own particular
flights of fancy, but ours go more or less like most, I imagine. My wife or I will
start, it doesn’t really matter who: Our little girl’s going to be so smart. Oh yes,
we’ll teach her everything we can. She’ll be so gentle, so good-hearted. These
things are very important to a good life, we agree. And of course, look at that
skin, like chai, those eyes, she’ll be so pretty. I mean, wow. Yeah, we’ll have to
put locks on the doors when she’s a teenager. And there the conversation takes a
little turn. But not too pretty, right? Yeah, we wouldn’t want that. We both sit
back, and the conversation moves on to something else. This is what it comes
down to: I can’t imagine anyone wishing limits on a son.
Unfortunately, it’s a problem the Internet is surely making worse: for The
Beauty Myth, social media signals Judgment Day. Your picture is attached to
practically everything, certainly every résumé, every application, every byline. If
people care about what you are doing, they will find out what you look like. Not
because they should, but because they can—Facebook and LinkedIn have
essentially extended OkCupid’s Love Is Blind problem to everything. Even just
ten years ago, it was almost impossible to tie the average person’s name to her
photograph; now you just Google the words—everyone does—and up pops a
thumbnail from a social network. We’ve all had to pick through snapshots for
that “best” one. Choose wisely, friends, because it defines you in a way it never
has before. There’s a momentum to the trend that might not be obvious to people
who work outside the industry. The new design standard of the last two or three
years, more open and more photocentric—what I think of as “Pinteresty”—is
making not just pictures, but beauty specifically more important. OkCupid
recently made a change for some photo displays, going from the size of the black
box to that of the red, below:
The designers just wanted the page to look more modern. What they didn’t
anticipate (and later had to mitigate) was the following: all those extra pixels
allowed the pretty faces to outshine the others all the more. The rich got richer. It
was the web-design equivalent of American domestic policy.
Given this pressure it’s no wonder that body-image blogs are so prevalent.
And that posts tagged like #thinspiration #thinspo #loseweight #keeplosing
#proana #thighgap became so common that both Tumblr and Pinterest
(independent of each other) had to alter their Terms of Service to ban this kind
of content. If you’re wondering what the last two hashtags are, #proana is short
for “pro anorexia”—people in favor of starvation as a weight-loss technique.
Meanwhile, #thighgap refers to having thighs so thin that they do not touch
when you stand with your feet and knees together. It’s a trait fetishized by
teenage girls. Quite apart from the questionable desirability, it’s biologically
impossible for most of them. The full depravity of the phenomenon can’t hit you
until you search for these tags yourself and are confronted with an unending
page of broken bodies tilting at the camera—not only are the “inspiring” women
deathly thin, they are also frequently in lingerie, bikinis, underwear. The blogs,
created by women, are truly the epitome of the male gaze—and I say this as a
person reflexively skeptical of the language of the academic left.
Tumblr and Pinterest banning the content didn’t solve anything, of course,
least of all their users’ body-image issues, so the sites are now taking another
approach. Because these blogs are tagged, they are able to intervene
algorithmically—search for thighgap on Tumblr and the screen goes blank, an
overlay appearing:
“if you or someone you know is dealing with an eating disorder …”
A link to help and resources follows. It is a small measure, but before the
behavior was digitized, there was practically no way to get directly at this
problem, at least not until visible damage had already occurred. There was only
rumor—an ear at the bathroom door, perhaps a parent’s sad suspicion. Data is
about how we’re really feeling—feeling about one another, yes, but also about
ourselves. If it finds divides in our culture, our politics, our habits, our tribes, it
finds divides within us, too. And that’s a hopeful thought, because for anything
to be made whole, the first step is to know what’s missing.
1 I foreground trend lines here because the data is slightly sparser and therefore more noisy than usual. This
sample is ≈5,000 people.
2 The study of beauty by traditional methods is especially susceptible to the problem of insufficiency. If
your research topic is, say, wealth, you can very easily get a measure of someone’s net worth or income and
then move on to the dependent trait you want to look at. But to study beauty, first you have to determine
how good-looking your subjects are, which is a resource-intensive process. Beauty being so wildly
subjective (as opposed to, say, hair color, where if you crowdsourced it, you might get slight variations
—brown, brunette, chestnut—that are essentially synonymous), you get wide swings in opinion that can
only be absorbed by sampling a large, diverse research set. As we’ve seen with WEIRDness earlier, that has
not been a strength of past academic research.
8.
It’s What’s Inside That Counts
There used to be two ways to figure out what a person really
thinks. One, you caught her in an unguarded moment. You snooped around, you
provoked, you constructed some pretext in a laboratory, you did whatever you
could do to get your subject to forget she was being watched. Research like that
was probably a lot of fun—a lab coat, a hidden camera … who knows, a fake
mustache—but on a large scale, it was impossible. So for data en masse, you had
only option two: to ask a question and hope for an honest answer. That’s been
the popular standard since Gallup formed the American Institute of Public
Opinion in 1935.
Unfortunately, surveys have historically been unable to uncover true attitudes
on topics such as race, sexual behavior, drug use, and even bodily functions,
because respondents edit their answers. Observed behavioral data is very useful,
as we’ve already seen. But there are some things—thoughts, beliefs—that don’t
entail an explicit action. And often the ugliest, most divisive, attitudes remain
behind a veil of ego and cultural norms that is almost impossible to draw back, at
least through direct questioning. It’s a social scientist’s curse—what you most
want to get at is exactly what your subjects are most eager to hide. This tendency
is called social desirability bias, and it’s well documented: the world over,
respondents answer questions in ways that make them look good. The most
famous case was the so-called Bradley effect: in 1982, California voters told exit
pollsters they had elected a black governor, Tom Bradley, by a significant
margin, but in the privacy of the ballot box they had actually given his white
opponent a narrow victory. Throughout the ’80s and ’90s, black candidates often
received more support in polls than in actual elections. Problems beyond racism,
like depression and addiction, are similarly difficult to diagnose at a societal
level because people can’t be honest about them. Even on OkCupid’s match
questions—which are by and large unseen by anyone but the answerer—the
users are just unwilling to own up to certain attitudes, even ones they in fact act
upon elsewhere on the site. The mere act of asking elicits self-censoring. Almost
every site that registers opinions or collects descriptive data has the same
problem. But there is one place that doesn’t need to ask for anything, and so the
data is set free: With search, there is no ask. You just tell.
Google’s only prompt is that famously open page, with its lone entry form
—that slim rectangle of emptiness, cursor parked and ready, just waiting for
your thoughts. The company’s business is to help people find stuff in the vast
thicket of the Internet, and it’s done that spectacularly. But almost as an
afterthought to its world-beating success, as users enter each new desire into the
database, Google has become a repository for humanity’s collective id. It hears
our confessions, our concerns, our secrets. It’s doctor, priest, psychiatrist,
confidante, and above all, Google doesn’t have to ask us for a thing, because the
question is always implied in the blank space of the interface: Hey, what’s on
your mind? Ahab and his whale, Arthur and his grail. What a person searches for
often gives you the person himself. The trick till now has been, How can we see
the search?
Since 2008, Google has provided that insight with its Google Trends tool. It
allows anyone to query their aggregated search database, and with the right
phrasing and a little cross-tabulation, you can use it to extract an excellent
sample of the private mind, of the internal workings that have until now
remained off-limits to research since research began. Since the service launched,
scientists have used Google Trends to predict the stock market, uncover drivers
of economic productivity (richer countries are more concerned with the future
than the past), and most famously, track epidemics of flu and dengue fever in
real time—and thereby stanch them as quickly. When people are getting sick,
they search for symptoms and remedies. Google Flu senses what’s afoot and
alerts the CDC.
The site also records other kinds of virulence. Because there is no asking, and
unlike on social sites, no other person on the other end of the line, people
unleash their vilest impulses into Google. “Nigger,” for example, is a common
search term—included in 7 million searches a year. In the United States, the
search volume is highest where you might expect—West Virginia—but it’s
steady throughout the country. Brooklyn has few things in common with the
town I grew up in, Little Rock, but this is one—“nigger” is as common in New
York City as it is in central Arkansas, and as common in Chicago as it is in
Fresno.1 Judging by search volume, the word is literally more American than
“apple pie”—by 30 percent. And, tellingly, it appears much more often in
Google than it does in a more public venue for the psyche, Twitter. Using
“nigga” as a control, since it’s similar in meaning but lacks the baggage,
“nigger” appears about 30 times more often in search than in social media.
Unlike the acute cycles of disease, racism runs a slow, grinding course—
working at the generational, not the metabolic level—and it’s one of the few
places where we can begin to see data’s broad longitudinal possibilities. Further,
tying the ebb and flow in searches to real-world events allows us to unlock some
of the emotional shading behind the data. For example, if you plot searches for
the word “nigger” over the 2008 campaign cycle, you can watch the country
come to grips with the prospect of a black president.
Working through the six red peaks, from left to right you see: Super Tuesday
on February 5, followed by the bitterly contested Pennsylvania primary on April
22. On June 6, searches hit a new high. Hillary suspended her campaign, and
Obama won the nomination. On July 15, complicating the data (and indeed the
moral discussion), Nas released an album whose unofficial title was Nigger, and
it went to number one. But even in the wake of that confounding event, overall
search volume plummeted as the fact of Obama’s ascendancy settled in. Racial
and even political tension dissipated while the nominees, neither yet official,
positioned themselves for the fall. In fact, the volume of racially charged
searches reached its lowest point over the whole campaign the week of the
Republican National Convention in early September.2
Having hit a minimum there, however, animus built back quickly to the norm,
then exploded on election night itself, when searches for “nigger” hit a level
never since equaled. The next day, when America woke up to the confirmed
reality of a black president, roughly 1 in 100 searches for “Obama” also included
the epithet or “KKK” in the query string. But almost immediately afterward the
volume of racially charged searches dropped sharply, and except for one last
gasp of anger at the inauguration, that lower level (25 percent below the preObama status quo) has held. You hear a lot about our “national conversation” on
race; when you look at the data, you see it’s really more a series of national
convulsions. But you also see that for all the failed promise of his famous
byword, Obama did change the course of our nation’s favorite epithet:
There have been, in fact, only three true jumps in “nigger” searches during the
Obama presidency. The first was driven by the kind of what-the-fact that Tea
Party politicians seem to specialize in: volume spiked in October 2011, the week
the world discovered that Texas governor Rick Perry has a “Niggerhead Lake”
on his property. The remaining two peaks, both comparable to Obama’s election
night in height and suddenness, were the bookends to a single story. The first hit
the servers in late March 2012, and the other the last week of June the following
year. They coincide with, first, Trayvon Martin’s parents bringing their son’s
death to national attention, and, second, when the prosecution made its case
against George Zimmerman—perhaps the two times since Obama’s first
campaign that whiteness felt most attacked. There was no comparable spike
during the defense phase of the proceedings, nor at the verdict. And, like they
did in the aftermath of the 2008 election, searches hit a new low right after the
acquittal, again showing the cycle of clench and catharsis that passes for race
relations in the United States.
When you’re out hunting for racially charged words, “nigger” is the obvious
place to start, but very quickly you find there isn’t much else of significance out
there; it’s really the alpha and omega of hate speech. Other awful terms like
“spic” and “chink” are so seldom used that there’s comparatively little data to
analyze. It’s not the epithets themselves that are the most meaningful, anyhow—
it’s the mind-set behind them, a truth you can see in the way the freight of the
word “nigger” changes with the identity of the speaker. If it were Toby Keith
and not Nas releasing that album in 2008, you’d have a much different story on
your hands. To that end, Google’s autocomplete function is useful; it gives
whole thoughts rather than just a context-free word.
If you’re not familiar with autocomplete, when you begin typing a phrase, for
example “Who is the …” Google offers to finish your thought with the text from
other popular searches. Type in “Who is the …” and it suggests “… richest man
in the world.” Tinker with it a bit, and it’ll give you a peek at humanity
wondering how the other half lives.
Why do women …
… cheat?
… have periods?
… wear high heels?
Why do men …
… pull away?
… fall in love?
… lie?
And when you start fishing for stereotypes, it’s like playing the game Taboo,
but without any taboos. Why do black people … like fried chicken? Why do
Muslims … hate America? Why do Asians … look alike? Autocomplete gives
you this kind of stuff—those are verbatim examples. In fact, one such result,
“Why Do White People Have Thin Lips?,” is the title of a recent research paper
that explores the dual purpose the feature serves: it reveals trends, of course, but
because of Google’s ubiquity it has the power to set them as well. The paper
suggests that autocomplete will eventually perpetuate the stereotypes it should
only reflect, and it’s easy to see how: a user types an unrelated question, only to
have other people’s prejudices jump in the way. For example, “Why do
gay … couples look alike?” was not a stereotype I was aware of until just now.
It’s the site acting not as Big Brother but as Older Brother, giving you mental
cigarettes.
When you turn the autocomplete queries inward, you get still another view of
humanity. It’s like standing alongside someone in front of his bathroom mirror.
Go to your search bar with:
“Why is my a …” then
“Why is my b …” and so on
and Google will complete your prompts with an alphabet of troubles, including
this brilliant run:
why is my stool green
why is my tongue white
why is my urine cloudy
why is my vagina itchy
All of which ailments, I have to point out, are probably the result of sitting at a
computer for too damn long.
So in all these ragged ways, our hidden thoughts are becoming part of the
world. With a little creative typing, a few workarounds, and some math, we are
giving humanity’s inner monologue a wider audience. We bring out the hurtful
as well as the ridiculous parts of ourselves, and for those hurtful impulses, search
data provides much-needed exposure. It is no longer publicly acceptable to say
racist things, but we can now know they’re still being spoken even when social
desirability bias might tell us otherwise. Moreover, though our power to detect
latent, hidden attitudes is new, our power to exploit them is not, which is why
this data is all the more important. I’ll let Republican strategist Lee Atwater
explain; below he’s discussing his party’s so-called Southern Strategy in an
interview with political scientist Alexander P. Lamis. He said this in 1981, as a
member of the Reagan administration:
You start out in 1954 by saying, “Nigger, nigger, nigger.” By 1968, you
can’t say “nigger”—that hurts you. Backfires. So you say stuff like forced
busing, states’ rights and all that stuff. You’re getting so abstract now [that]
you’re talking about cutting taxes, and all these things you’re talking about
are totally economic things and a by-product of them is [that] blacks get
hurt worse than whites.
Atwater thought he was speaking off the record (“Now, y’all aren’t quoting
me on this?”). Search data means we don’t have to wait for such accidents to
examine the disconnect between the public and private conversation on a topic
like race. It shows we’re heading toward a better world. It also shows we have
far to go.
Let’s pick up where we left Obama, on Inauguration Day, 2009. There was a
lot of hopeful talk then that the United States had become a “post-racial” society,
and it wasn’t necessarily a far-fetched idea. At its core, the “post-racial” story
was an attempt to extrapolate the success of Obama’s campaign to other corners
of American life, and to say that his victory proved that “race wasn’t a factor” in
our lives, not anymore.
Despite that hopeful possibility, Seth Stephens-Davidowitz at Google
concluded that Obama’s race probably cost him 3 to 5 percentage points of the
popular vote in 2008—and the loss wasn’t from Republicans but from people
who otherwise would’ve voted for a white Democrat like John Kerry. At the
high end of the range, that 5 percent swing would’ve altered well over half the
elections since World War II, and it’s a result we could never have detected
without search data. The researcher’s brainstorm was to go back before Obama
entered the national political picture, to 2004–2007, and mine Google Trends for
preexisting racial attitudes. (That keeps dislike of Obama himself from clouding
the picture.) Using that data to get a state-by-state “racial animus index,” he
could then compare that index against Obama’s eventual vote totals and against
the expected outcome for a generic (i.e., white) Democratic candidate (for which
of course there is ample previous data). Reliably, the higher the animus index,
the worse Obama performed. Here’s an example of the method in the words of
the man who did the work:
Consider two media markets, Denver and Wheeling (which is a market
evenly split between Ohio and West Virginia). Mr. Kerry received roughly
50 percent of the votes in both markets. Based on the large gains for
Democrats in 2008, Mr. Obama should have received about 57 percent of
votes in both Denver and Wheeling. Denver and Wheeling, though, exhibit
different racial attitudes. Denver had the fourth lowest racially charged
search rate in the country. Mr. Obama won 57 percent of the vote there, just
as predicted. Wheeling had the seventh highest racially charged search rate
in the country. Mr. Obama won less than 48 percent of the Wheeling vote.
Historically, a presidential candidate can expect a modest boost, about 2
percentage points, in the popular vote in his home state. Because of racial
animus, John McCain in 2008 had better than home-state advantage throughout
the entire country. If you’re looking for evidence of whiteness as a leg-up in
American life, this is it. McCain was the nation’s favorite son for no other reason
than he was pitted against a black man.
In my opinion, Muhammad Ali is one of the bravest Americans. In 1967, as
heavyweight champion, he refused to serve in Vietnam and was not only
stripped of his title but banned from the sport for three and a half years. He lost
the prime of his career, and received a five-year prison sentence (that took the
Supreme Court to overturn), because of what he believed in. It’s a stand
unimaginable from today’s political leaders, let alone our athletes and
celebrities. From Kanye to Glenn Beck to Rachel Maddow to Sarah Palin, you
get plenty of anger, but little sacrifice. We can each have our own take on Ali’s
stance against Vietnam—and as the son of a veteran, Huê´ ’69, I know at least
one person who disagrees with mine—but data like this can help anyone
understand why he took it. As Ali said at the time, “No Viet-Cong ever called me
nigger,” and he was probably right. But imagine, had Google existed then, what
would’ve been going into American search bars. And imagine the home-state
disadvantage of a black man in those days.
It remains to be seen where attitudes will go next. For all the above, Obama
did win, and as depressing as some of this stuff is, there’s a lot to be encouraged
about—for one thing, there was no evidence that bias hurt the president again in
2012, though he was a known quantity by then, perhaps less “a black man” than
“Barack Obama.” One thing that gets lost in all the aggregation throughout this
book is that on an individual level, the personal effects of these broad social
forces are often very subtle. To speak to the data you’ve seen in a previous
chapter, OkCupid’s many black users have a fine experience on the site—each
one of them gets dates and rejection like anyone else. They just get, collectively,
more of the latter. When you go person-by-person, any individual’s experience
is too small and too varied to conclusively say anything “racial” has happened. It
could be your skin, or it could be just you. On the other side of it, it’s laughable
to think of one red-faced guy searching for “nigger jokes” because Barack
Obama got elected. But it’s a lot less funny when you can see that he’s one of
thousands and thousands making the same search. And it’s less funny still when
you see the large effect these private attitudes can still have, even in public life.
Thus the story of just one of us versus the story of us all. That’s why data like
this is necessary—it ends arguments that anecdotes could never win. It provides
facts that need facing.
I know some people who only read good books—and by that I mean things
that come recommended: by friends, teachers, reviewers, Amazon. It makes
sense; reading is slow, time is precious, why risk it? But that’s not my style. I
like history, and when I go to the bookstore, I just grab a bunch of random stuff
from the section shelves and see what sticks. Reader, I have read some bullshit.
And too many books on Napoleon. But among many serendipitous discoveries,
A People’s History of the United States is my favorite. Yes, I know now it’s a
classic, but that doesn’t change the fact that I’d never heard of the book until I
pulled it down. Google Books describes it well: it’s a chronicle of “American
history from the bottom up”—and where most books treat leaders and big
events, A People’s History shows us the homes, shops, farms, factories, and
smaller worries of yesteryear. The thing is, as much as I love that book, and as
much as it turns the schoolhouse version of American history on its head,
Howard Zinn could still only tell us what he could see, the observable actions,
the words spoken aloud. The hearts of women and men were beyond him. In the
stress of the Cuban Missile Crisis, in the boredom of the trenches, in the
liberation of the Pill—for all the moments of quiet joy and interior anguish lost
to history, what if we had the data we have now? How much richer would our
understanding be?
1 Google Trends expresses a search’s popularity with a simple index number proportional to the number of
searches for the word or phrase. The indices for this epithet are within 10 percent of each other for the listed
metro areas. “Nigga” is not included, since most of its related searches are for rap lyrics (the exact search
query for my data throughout this chapter was: “nigger −nigga −song”). The top related searches for
“nigger” are, by far, “jokes” and “nigger jokes.” For my racial search analysis, I’m relying on a method
originated by Seth Stephens-Davidowitz, a data scientist and economist at Google. Reporting from his
inside view of the data, he writes: “A huge proportion of the searches [for “nigger”] were for jokes about
African Americans.” He uses public and anonymous data for his research.
2 This wasn’t just people going on vacation: neutral terms like “pasta,” “pizza,” “family,” and “truck” hold
steady throughout the year.
9.
Days of Rage
On New Year’s Eve, bored on her couch and waiting for the ball to
drop, Safiyyah Nawaz tweeted a silly joke.
$afiyyah @safiyyahn
this beautiful earth is now 2014 years old, amazing
She got 16,000 retweets, almost all of them in the next twenty-four hours. For
reference, Katy Perry’s Happy New Year wish to her 49 million followers got
just over 19,000. Lady Gaga’s, which also announced a long-awaited video, got
20,000. Safiyyah Nawaz is not some emerging world pop star, and this isn’t the
story of Twitter empowering upstarts to challenge the cultural order. If you
haven’t heard of Safiyyah, that’s because she’s a North Carolina high school
student whose joke, the exact words above, made Twitter explode.
At first it was people verbally scratching their heads, wondering if she was
serious, but if you watch the tweets from that night go by, each retweeter a
further degree removed from Safiyyah the human being, and each more aware
that his or her ridicule was part of a phenomenon—this from watching the
retweet number tick up—you can actually see the digital crowd become a mob.
In short order, the amused LOLs became OMGs became WTFs, and then stuff
like this took over:
Cocaine Burger @Cocaine_Burger
@safiyyahn Kill yourself
Rick Huijbers @HARDEBAKSTEEN
@safiyyahn kill yourself you stupid motherfuck
It went, as Gawker put it in their coverage, from dumb to #dumbbitch in a
matter of minutes. Given the violence of the reaction, Ms. Nawaz handled the
experience pretty well for a seventeen-year-old, and later she sized up the outcry
perfectly:
$afiyyahn @safiyyahn
young folks these days b really passionate about the tru age of
the earth
Nawaz was unaware of it, but she had famous company in the crosshairs. Just
fifteen minutes before she’d tweeted her joke, comedian Natasha Leggero was in
Times Square, on television with Carson Daly, bantering about the SpaghettiOs
Pearl Harbor Day PR campaign. The brand had come under fire for encouraging
citizens to remember the fallen via purchase of canned spaghetti—yes, this is
what the world has come to—and she said, “It sucks that the only survivors of
Pearl Harbor are being mocked by the only food they can still chew.”
Host and guest laughed and moved on to other things, unaware that Natasha,
too, had inadvertently brushed against the highly sensitive On switch of the
Internet-rage machine. It sputtered into righteous action; Ms. Leggero later
posted on Tumblr several choice examples of the tweets she got. Stuff like:
Mike Oswald @SDPStudio
@natashaleggero What a vile whore you are.
Mark Tichenor @hotrod607
@natashaleggero Fuck You, you disrespectful cunt
And my personal favorite, which, should the Internet ever die, will be its
epitaph:
Chris McAllister @macdawg22
@natashaleggero your a stupid ignorant whore.
I was paying special attention to these two episodes because something
similar had just happened to a coworker of mine. On December 20, Justine
Sacco, who was director of communications at OkCupid’s parent company, IAC,
was at Heathrow, waiting for a connecting flight to Johannesburg. She boarded
the plane, sat down in her seat, and typed:
Justine Sacco @justinesacco
Going to Africa. Hope I don’t get AIDS. Just kidding. I’m white!
Then she turned off her phone. Her tweet was less obviously a joke than the
other two examples and at best—at best—it was a clumsy dig at white privilege.
But what started with justified head-shaking at her cluelessness quickly became
a carnival of intense personal hatred. She got the usual threats and insults, but
the attack aimed for more than her Twitter persona. Pictures of her family were
circulated online, along with their whereabouts. Men called her nephews,
threatening to rape them. People gathered at the Johannesburg airport to await
her plane. Her inability to respond while aloft added an extra jolt of enthusiasm
to the takedown. About midway through her flight #HasJustineLandedYet was
coined and became a top trending topic on Twitter. Google searches for her
name began to automatically return her flight number and its arrival time
because that’s what people were searching for—search algorithms had again
held up a mirror. For the eleven hours Justine hung in the air, the Internet waited
dry-mouthed and bloodthirsty for the moment she would reconnect to find her
life in ruins.
Ron Geraci @RonGeraci
It’s like 2 million people are waiting for her with the lights off to
see her expression as the earth explodes.
I’m Gary @noyokono
#HasJustineLandedYet People haven’t eagerly anticipated a
plane landing this much since Amelia Earhart.
V. Hussein Savage @Kennymack1971
Aw hell.… lemme finish this work grab a 6 pack and some BBQ
wings. It’s about to be on…
#HasJustineLandedYet
Their quarry here was someone with a few hundred followers and no public
profile. I didn’t know Justine all that well, but I had enjoyed working with her,
and watching the obvious excitement people got from the pain and fear they
were about to cause sickened me.
Like a fool, I went to Facebook to vent. My post wasn’t up ten minutes before
an acquaintance (and future former Facebook friend), who at that point I hadn’t
spoken to in fifteen years, commented “her father is a billionaire” and implied
that that somehow justified her personal destruction.1 But of course her father
isn’t a billionaire—that was just another rumor that had attached itself to the
story. It was like running into a mob at a stoning, trying to drag people away,
finding someone you know—whew, finally, a guy you can reason with—only to
have him yell, wide-eyed, “Dude, check out all these rocks!”
The stoning metaphor comes up again and again when you read the
commentary on episodes like these. It’s no coincidence that it’s the death penalty
of choice for the ancient religions: there is no single executioner; the community
carries out the punishment. No one can say who struck the fatal blow, because
everyone did together.2 For a burgeoning tribe, fighting to preserve itself and its
god in a hostile world, what better prescription could there be? There is strength
in collective guilt, and guilt is diffused in the sharing. Extirpate the Other and
make yourselves whole again.
In Justine’s case, people on three continents had assembled to destroy her.
Pulling self-descriptions from just a handful of their Twitter bios you find it
takes all types: Lobbyist. Communist. Hater. Aspie. Leader. Nature Enthusiast.
Blogger. Gator. Dad. Writer. Imperfect Christian. Professional Shade Detector.
Pop Culture Virtuoso. Daughter of the Sea, Sister to the Wind. These people had
nothing in common but a target and a hashtag at hand, and they got the blood
they came for. Justine lost her job. BuzzFeed put her face up on their front page
with a big “LOL” over it.
The reach of social media makes the force of these gatherings immense.
Within twenty-four hours of her tweet, Safiyyah had been called down in front
of 7.4 million people. And 62 million saw #HasJustineLandedYet that first day.
Not everyone under the curve read the tweets or cared, but many did, and all
were in some way a witness.
Sir Qwap Qwap @BeardedHistoria
Literally every one of the first 20 tweets on my home feed has
#HasJustineLandedYet. I must have missed something, Tweetfiends.
It’s worth pointing out that this fantastic volume should be an embarrassment
to social media—evidence not just of its power but of how hollow that power
can be. In Justine’s case, AIDS, racism, and the stubborn, shameful poverty of
postcolonial Africa are all enormous problems that tweeting does absolutely
nothing to solve.
We may think of human sacrifice as something from a savage past, and the
physical act might now only exist in films about temples and doom, but the
instinct remains within us, seemingly burned by deep time into the reaches of the
animal mind. When food is scarce, lions kill their cubs. Fish eat their own eggs.
In multiple human pregnancies a womb will sometimes absorb a fetus to
preserve the others. To destroy the one for the many is possibly a practice as old
as life itself. Now that this ritual is carried out in bits (and thankfully with no
actual blood on anyone’s hands, though you get the idea, reading some of these
tweets, that people view this as a bug rather than a feature), it’s become a topic
we can rigorously study for the first time. Social scientists have devoted
considerable energy to the question of why and how negative ideas spread, and
the Internet has given them both limitless source material and a powerful
tracking mechanism. Marine biologists tag sharks in the wild to understand their
movements and to limit their threat to humans.3 Here it’s the words that have
teeth. My three cases above aren’t precisely rumors or gossip, but mob outrage
follows many of the same pathways, both neurological and person-to-person,
and the science of rumors can help us understand what has happened to people
like Natasha, Safiyyah, and Justine—and why.
Rumors are mentioned in our earliest texts. The archaic pantheons—Norse,
Egyptian, Greek—all have a god dedicated to the dark art of gossip. The book of
Proverbs treats the topic thoroughly; one verse from many cautions that “a man
who lacks judgment derides his neighbor, but a man of understanding holds his
tongue.” “Judge not lest you be judged” is one of the most famous phrases in the
whole Bible. Several sources maintain that the Romans enshrined a goddess
named “Rumor”—a winged demon with a hundred eyes and a hundred mouths
who spoke only the most hurtful side of the truth. Appropriately enough, I can’t
seem to confirm this.
Evolutionary biologists believe that gossip and rumors arose from our
ancestors’ need to understand their surroundings through speech. The theory is,
when ancient man had to figure out if x was true, language gave him a way to
investigate. So he talked about it. And, true or false, word spread. Rumors—
essentially group speculation over the truth of an idea—became a way to build
bonds and social capital. Stories create status for those who share them,
especially when they concern important individuals, because information about
powerful people is a form of power itself.
But the advent of social media has changed the calculus in a couple ways.
First, it gives us metrics—follower counts, retweet counts, favorites counts—to
judge our status. Be the first to spread the news, get more retweets. Say
something especially cutting, and your followers applaud your wit. The social
capital you build by sharing information is now explicit; in fact, it’s in little
numbers that increment before your very eyes. Writing in the Boston Globe,
Jesse Singal was discussing the motivations of traditional person-to-person
gossip but might’ve easily been talking about Twitter when he said, “To the
extent people do have an agenda in spreading rumors it’s directed more at the
people they’re spreading them to, rather than at the subject of the rumor.” The
Internet gives people a wider audience than ever before.
The second change is that the Internet has also made everyone a public figure.
High-status individuals were once chieftains, and then celebrities and presidents,
but, here, the leveling scythe of technology shows its obverse edge. If anyone
can become an overnight celebrity, anyone can become an overnight leper. One
of my least favorite Internet-evangelist talking points is about technology
“empowering” people—inevitably the most empowered of all is the speaker and
his investors. But here we find some truth in the cliché—social media empowers
you to the extent that it makes you worth tearing down. At the same time, it
gives everyone else the tools to do it. Demon Rumor now has a million mouths.
So much of what makes the Internet useful for communication—asynchrony,
anonymity, escapism, a lack of central authority—also makes it frightening.
People can act however they want (and say whatever they want) without
consequences, a phenomenon first studied by John Suler, a professor of
psychology at Rider University. His name for it is the “online disinhibition
effect.” The webcomic Penny Arcade puts it a little better:
Greater Internet Fuckwad Theory
normal person + anonymity + audience = total fuckwad
But it’s not the vitriol, nor even the anonymity, that’s unique here. The
Internet hasn’t been quite the revolution in trollery you’d think. The old CB
radio channels that truckers used were notoriously filled with racist diatribes and
masturbation fantasy.4 Before caller ID took away that necessary additive,
anonymity, the Jerky Boys were churning out fuckwaddedness for decades.
People still flame one another on ham radio—as if being a ham radio operator in
2014 isn’t burn enough. No, the unique thing that the Internet brings to our long
history of negativity is that we can finally constructively respond to it. In some
way, Tumblr’s thighgap intervention discussed in chapter 7 is just a special case
of what’s now broadly possible. We can pinpoint the speaker, the words, the
moment, even the latitude and longitude of human communication. As I pointed
out earlier, by 2015, Twitter users will have exchanged more words than have
ever been printed. The question is how to harness the chatter.
The government has the greatest vested interest in tracking negativity.
Mathematical models already exist to predict the outcome of armed conflict—
how long it will last, who will win, and how many people will die—and the
models of late have learned to accommodate guerrilla warfare, since that’s the
shape of today’s war. But armed insurgency is often preceded by unarmed unrest
—which itself is often propagated, even coordinated, through social media.5
Those nascent movements, being digitized, have attracted the attention of
researchers.
Using Western movements as his test subjects, MIT’s Peter Gloor has
developed software to track the ebb and flow of sentiment in a network of
protestors. He calls it Condor, because that’s what projects like this always seem
to be called: Condor, spirit-bird of government grants. In any event, the software
first establishes a group’s central personalities by looking at its social graph—
much like we portrayed a marriage as edges and nodes before, the software lays
out the network, then algorithmically determines its most important dots. Next, it
looks at what those dots are saying. Condor has found that while the foci of a
movement are positive in their word choice, the movement is vibrant. But
negative words like “hate,” “not,” “lame,” and “never” signal decline, and when,
as The Economist put it, “complaints about idiots in one’s own movement or
such infelicities as the theft of beer by a fellow demonstrator” begin to appear,
the movement is all but over. Oh, Occupy!
As for deciphering the aims of unrest, which is where this technology can
move beyond mere spying and into doing some good, similar kinds of textual
analysis have been used to determine, for example, which Egyptian towns will
be most upset by border incidents with Israel, and to pinpoint water insecurity in
a drought-stricken countryside.
Any software that follows the thread of a thought through a network must
track not only the idea but the “susceptibility” of people exposed to it. It must
see what takes hold, what gets repeated, and who moves it along. Relaying
someone else’s opinion isn’t unique to the Internet any more than negativity is:
television and radio made “talking points” into a phrase long before AOL came
along, let alone Twitter. Rush Limbaugh’s staunchest fans call themselves
“Dittoheads”—but nothing makes parroting an idea more simple, or more
trackable, than the Like, the Ping, the Reblog, or the Retweet button. Remember:
27.5 percent of Twitter’s 500 million tweets a day are retweets, people just
passing along someone else’s thought.
Facebook’s data team investigated their version of the phenomenon, tracing
the evolution of a single status update from the health-care debates in 2009
through the network:
No one should die because they cannot afford health care, and no one
should go broke because they get sick. If you agree, post this as your
status for the rest of the day.
This was reposted, verbatim, more than 470,000 times and also spawned
121,605 different variants, which themselves received about 800,000 more posts.
Someone who didn’t quite feel that the update spoke for him would change it
slightly, and versions spread outward into different social circles. When you put
each version against the political bias of the people posting it (–2.0 is maximally
liberal, +2.0 conservative) not only do you get an interesting look at the
American political spectrum—extremes of right and left, plus a center that has
opted-out of the discussion—but you also see how political belief translates into
words. People at the top and bottom of this list use the same framework to speak
at cross-purposes:
No one should …
political bias of the person
posting
… die because they cannot afford health care … –0.87 more liberal
… be frozen in carbonite because they couldn’t pay Jabba
the Hutt …
–0.37
… die because of zombies if they cannot afford a shotgun … –0.30
… have to worry about dying tomorrow, but cancer patients
do …
–0.02
… be without a beer because they cannot afford one … +0.22
… die because the government is involved with health
care …
+0.88
… die because Obamacare rations their health care … +0.96
… go broke because government taxes and spends … +0.97 more conservative
In 1950, at the dawn of the age of television, the American Political Science
Association actually called for more polarization in national politics—the parties
had grown too close together, the electorate didn’t have clear choices. The
APSA got their wish, and in the old genie-style, too, with plenty to regret about
its granting. Now, sixty years later, we’re more divided than ever, and you can
track this, too, through the words. The repetition of partisan speech both in
Congress itself and in print (as tracked through Google Books) correlates with
political gridlock, which is at an all-time high. That we’re divided might be the
only thing we can, in fact, agree on.
This paradox was driven home to me when I turned to Facebook in the
aftermath of Justine’s tweet. In my post was a link to an article from
breitbart.com—the namesake site of Tea Party instigator Andrew Breitbart. A lot
about the article was regrettable, but the author was one of the only people
pointing out how out-of-proportion the reaction was. I’d always imagined
uncritical outrage as a vice of the political right—I’d hear about the ridiculous
“War on Christmas” or the belief that Obama was “taking people’s guns away”
and think, What fools these people are to believe this stuff! Why talk about
things in such extreme terms? Why look at something only in the worst possible
light? But it took this incident on Twitter to make me see that people on the
“left” could be just as self-righteously uninformed as anyone else. It was eyeopening, and shame on me for having them closed in the first place.
So theories aside—and the science is so new that no doubt Condor will look
like Zork in a few years—this, to me, is why the data generated from outrage
could ultimately be so important. It embodies (and therefore lets us study) the
contradictions inherent in us all. It shows we fight hardest against those who can
least fight back. And, above all, it runs to ground our age-old desire to raise
ourselves up by putting other people down. Scientists have established that the
drive is as old as time, but this doesn’t mean they understand it yet. As Gandhi
put it, “It has always been a mystery to me how men can feel themselves
honored by the humiliation of their fellow beings.”
I invite you to imagine when it will be a mystery no more. That will be the
real transformation—to know not just that people are cruel, and in what
amounts, and when, but why. Why we search for “nigger jokes” when a black
man wins; why inspiration is hollow-eyed, stripped, and, above all, #thin; why
people scream at each other about the true age of the earth. And why we seem to
define ourselves as much by what we hate as by what we love.
1 If Facebook ever gets tired of that minimalist f and wants a new logo, I suggest, on a blue background:
two white people arguing about what another white person said about Africa.
2 It would be interesting to see if residents of countries where stoning is still used as a real-world
punishment take as much joy in the digital version.
3 In Australia these tags are outfitted with transponders that notify local beachgoers when a shark is nearby.
The tags communicate to us … via Twitter.
4 And, as they do online, the users even had “handles.”
5 The Arab Spring, for example, was Twitter’s debut as a tool of global importance, and the service has also
facilitated protests in Guatemala, Moldova, Russia, and Ukraine.

10. Tall for an Asian
11. Ever Fallen in Love?
12. Know Your Place
13. Our Brand Could Be Your Life
14. Breadcrumbs
10.
Tall for an Asian
When I was applying to college, I had to write about myself.
I’m sure you did, too. I can’t even remember the question on the application
because whatever it was actually asking was beside the point. The essay was
there to get me to talk about Christian Rudder, so the Admissions people could
decide if they liked what they heard. As the Common Application now puts it:
“The personal essay helps us become acquainted with you as a person.”
Being a sucker for melodrama even then, I wrote about how sad I would be to
leave my dog behind when I went to school. We’d gotten Frosty when I was six,
so he and I had grown up together. But with dog years working like they do,
he’d gotten too old too fast. My family had moved around a lot, and he was that
last connection to deep childhood: clubhouses, neighborhood pools, friends; I’d
left them all in Houston, or Cleveland, or Louisville, but Frosty always came
with me. The next move, however, I knew I’d have to make on my own.
In any event, adrift in pathos and extra-large M. C. Escher T-shirts, I
completed my college application. I haven’t written many self-statements since,
but involved as I am in the business of understanding people I can’t help but
think back on my seventeen-year-old self and the essay he chose to write. Why
talk about Frosty and getting older? Why not talk about baseball? Or basketball?
Or tennis? Or rotisserie baseball? Or any other of my diverse interests? What
was it, when the prompt was “Who are you?” that made me respond like I did?
And, even more important, how were other kids answering the question?
Now, twenty years later, I find myself sitting on millions of essays—billions
of words—more or less written to answer that same prompt: “Who are you?”
And this body of text actually allows me to do the inverse of the college
application process. Instead of matching essays one at a time against a
preconceived ideal (i.e., “college material”), I can mush all the essays together
and see what ideals they reveal to me. There are times when a data set is so
robust that if you set up your analysis right, you don’t need to ask it questions—
it just tells you everything anyway. How do people describe themselves? What’s
important, what’s typical, what’s atypical? When everyone else gets a turn to put
down in words who they are, what identities do they sketch?
We’re going to look at broad categories here: black people, white people,
Asians, females, males, and so on. A problem in studying any particular group is
that you always bring your own prejudices and preconceptions along with you.
What you choose to notice, remember, and transcribe is as much a matter of how
you look as what’s actually there. In social science, knowledge, like water, often
takes the shape of its vessel. So if we want to take all the self-statements I’ve
collected and pull from them a sense of who the writers are—what makes
ethnicities and sexes and orientations unique—we’ll need to develop an
algorithm that takes the “us” out of it and leaves just the “them.”
OkCupid’s user-submitted profile essays are as close to personal selfsummaries as you’ll find. The prompts are open-ended:
“My self-summary …”
“I’m really good at …”
“The first things people usually notice about me are …”
“I spend a lot of time thinking about …”
And insofar as people try to put their best foot forward, they’re not at all unlike
college essays. I imagine many people approach them with the same sort of
dread. There are no length restrictions, no guidelines but for the prompts.
Altogether, people have given the site 3.2 billion words of self-description.
Moreover, unlike other big hunks of text—say, what Google Books has collected
—there are demographics behind every word: the age of the author, where she
lives, her race, and so on. But deriving a group identity for, say, Asian women
from the text isn’t quite as easy as counting up who types what the most, which
for the most part is how we’ve looked at text so far in this book. Counting words
just gets us this:
1. the
2. of
3. and
4. …
and so on down the line—basically that top 100 from the Oxford English Corpus
we saw before. Asian women, white men, and all English speakers use the same
pronouns and articles and prepositions to talk about themselves. To find out
what’s actually special to a particular group, and to them alone, we have to sort
the text a little differently.
I’ll use white men as my walk-through example, because I understand them
the best. The first step is to separate those white guys’ essays from everyone
else’s. Then, in the two sets of self-descriptions—white-guy and not—we order
all the words and phrases in the texts by how frequently they appear. We put
them into two lists, from most popular to least, and that gives us something like
the chart below. I’ve pulled out three examples and put them in their correct
places in the line; the full lists have about 360,000 phrases each:
Already we’re getting somewhere, but before we move on, there’s something
a little misleading about these plots that I want to address while the list is still
simple. No, it’s got nothing to do with Phish, though lord knows they’ve misled
many. It’s that “pizza” and “the” appear to be mentioned almost the same
number of times. Granted, pizza is the king of foods, but “the” is the absolute
most popular word in the English language. And in our data, while “the” is in its
rightful place at the top, “pizza” is seemingly right there with it, at the 98th
percentile. This makes it feel like something is wrong either with my data or
with my method, but the rankings of the words are correct. It’s just that humans
use language in an odd way: we are always repeating ourselves. So a very few
top-ranked words take up most of our writing. And, conversely, the frequency of
a word falls off very quickly as you go even a small distance from “most
popular.”
This counterintuitive relationship between the popularity of a word (its rank in
a given vocabulary) and the number of times it appears is described by
something called Zipf’s law, an observed statistical property of language that,
like so much of the best math, lies somewhere between miracle and
coincidence.1 It states that in any large body of text, a word’s popularity (its
place in the lexicon, with 1 being the highest ranking) multiplied by the number
of times it shows up, is the same for every word in the text. Or, very elegantly:
rank × number = constant
This law holds for the Bible, the collected lyrics of ’60s pop songs, the
canonical corpus of English literature (the Oxford English Corpus), and it
certainly holds for profile text. To see how well it works in practice even on a
highly idiosyncratic body of writing, here’s the law applied to James Joyce’s
Ulysses:2
word rank number of times it appears rank × number
’s 10 2,826 28,260
is 20 1,435 28,700
what 30 975 29,250
has 100 289 28,900
wife 200 140 28,000
Ireland 300 90 27,000
college 1,000 26 26,000
morn 5,000 5 25,000
builder 10,000 2 20,000
Zurich 29,055 1 29,055
The steady relationship between rank and number seems to be a property of
the mind as much as of language—as you can see above, it accommodates
arbitrary proper names, like “Ireland” and “Zurich,” and even words transcribed
from dialect, like “ ’s.”
And as further evidence of its deep connection with the human experience,
Zipf’s law also describes a wide variety of our social constructs: the sizes of
cities, for example, and income distribution across a population. What it means
for our purpose here is that because most of language is just a small body of
repeated patterns, the use of a word drops off rapidly. “The” appears on nearly
every profile. “Pizza” appears on about 1 in 14. “Phish,” even for white guys, for
whom it ranks way up at the 80th percentile, appears in less than 1 in 200
profiles. Now that we understand how rankings and usage frequency compare,
the next step is to use those rankings to our advantage.
Below, I’ve put the two lists at right angles, forming a square, and I have
plotted the words inside it using their popularity rankings on the two lists as
coordinates. I added some arrows around “Phish” to make it clear what I mean:
A word’s position here has dual meaning. The closer to the top it appears, the
more popular it is with white guys. The farther toward the right, the more
popular it is with everyone else. Adding a few more words to the chart will give
you a sense of how the geometry translates before I zoom out to the full corpus:
I’ve added a diagonal, yet again, to show parity in the data. The words near
the line are important to everyone equally. And the farther up and to the right the
words go, the more universally important they are. But remember, we’re not
looking for universals. We’re looking for particulars. We want to know what is
special to the people we’re considering: here, white guys. For that we need to
look to the upper left: the farther in that direction a word appears, the more often
white men use it, and the less often everyone else does. In fact, the closer a word
is to that remotest reach of white maleness, the top-left vertex of the square, the
more it typifies them and only them. Imagine a dot all the way in the corner: to
be there, the word would have to appear on every single white male profile and
at the same time never appear anywhere else. At least as far as words in a selfsummary go, that’s the platonic ideal of identity. This system, and that metric—
distance from the upper-left corner—gives the data a way to speak to us, to help
us understand how people are talking about themselves.
Because every data set has its quirks, researchers must often build tools from
scratch, as we have here. Whenever you do this, it’s good to check your method
against some familiar outcomes. Imagine a shipwright with a new boat: who
knows what’ll happen once it’s out on the open ocean—so best to check for
holes close to shore. Here, if we’d found “Kpop” (Korean pop) or “dreads” in
the upper left, in my supposed corner of white-manhood, it would be a strong
sign that either my data or my method was garbage. But as you can see, it’s
working perfectly.
So, finally, here’s what the whole corpus of words and phrases looks like:
I’ve circled the dot closest to that upper-left corner: that’s the white-male-est
thing a person can write about himself: my blue eyes. And getting a longer list of
the things that uniquely define white men is just a matter of walking out from
that vertex—for example, the thirty closest dots are the thirty things that are
most typical. The geometry finds the clichés for us.
I’ve made plots like this for everyone in my data set, not just white guys, and
using this same math I’ve gotten lists of their unique words and phrases, too. But
before I move to listing all this, I want to make one important point. Walking
through each combination of sex × ethnicity × orientation gives you 2 × 4 × 3 =
24 charts like the one above, and in all of them the mass of dots has this same
tapered shape from bottom left to top right. That is, the farther a phrase goes into
that upper-right corner, the closer to the diagonal it gets. What that means is that
we tend to agree on the things that are most important. As for the things we
don’t agree on, I’ve listed them in detail below. I’ll start with the men:3
most typical words for …
white men black men Latinos Asian men
my blue eyes dreads colombian tall for an asian
blonde hair jill scott salsa merengue asians
ween haitian cumbia taiwanese
brown hair soca una taiwan
hunting and fishing neo soul merengue bachata cantonese
allman brothers jamie foxx mana infernal affairs
woodworking zane banda seoul
campfire paid in full puertorican infernal
redneck nigga colombia shanghai
dropkick murphys luther vandross gusta boba
they might be giants coldest winter puerto rican kbbq
brewing beer tyler perry tejano kpop
robert heinlein swagg corridos badminton
tom robbins jerome bachata merengue kimchi
townes dreadlocks hector chungking express
old crow medicine show spike lee espa chou
mystery science theater holla at me por viet
skis menace to society salsa bachata jiro
sailboat brotha aventura dash berlin
around a fire shottas english and spanish ucsd
caddyshack boomerang musica beijing
blond hair nigerian espa ol hk
blond hair nigerian espa ol hk
bill bryson heartbeats como norwegian wood
wheelers anthony hamilton fiu jiro dreams of sushi
pogues gud pero lin
barenaked ladies wayans soledad philippines
mst3k dickey espanol noodle soup
truckers isley amor malaysian
jethro tull interracial muy for my next meal
canoe nigeria reggaeton gangnam style
Phish might’ve already given it away, but inside the white man rages a music
festival for lumberjacks.
As for the other three lists, I had never heard of Zane or Anthony Hamilton or
The Coldest Winter Ever or Chungking Express or Dash Berlin or a lot of the
above before my scripts coughed them up, and I’m not going to pretend that a
few minutes with Wikipedia can stand in for an understanding of a culture.
These are users speaking in their own voice, and I’m going to let them do just
that, but I will point out a few broad trends: white people differentiate
themselves mostly by their hair and eyes, Asians by their country of origin,
Latinos by their music. But because of the way the math is set up, the three nonwhite lists are evidence of cultures that I, as a white man, am not supposed to
know. Of course, we’re all familiar with Spike Lee and Beijing and Shanghai,
but these lists give us the “insiders’ ” view of a culture. It’s stuff an outsider
can’t get from autocomplete, or in any other top-down way, because you can’t
wonder at what you don’t realize is out there. “Why do Asian people like
Norwegian Wood?” isn’t a stereotype because not enough non-Asians are
familiar with the book (by Haruki Murakami) and movie. I thought it was just a
Beatles song, and if before this chapter someone had asked me if I’d seen
Norwegian Wood, I’d have said, “I don’t think they made videos back then.” The
lists above are our shibboleths. As such, they are something no one could
generate a priori, by typing things into Google Trends or by searching millions
of hashtags. Sometimes, it takes a blind algorithm to really see the data.
Here are the lists for women. As you can see, they’re very similar in spirit to
the male. Maybe a few more ballads.
most typical words for …
white women black women Asian women Latinas
my blue eyes soca taiwan latina
red hair and eric jerome dickey tall for an asian colombian
blonde hair and haitian philippines una
love to be outside imitation of life taiwanese cumbia
mudding zane beijing banda
campfire coldest winter ever coz tejano
four wheeling nigerian boba merengue bachata
phish interracial filipina gusta
hunting fishing rb and gospel cantonese puertorican
campfires five heartbeats asians colombia
green eyes and anita baker wong kar wai mana
redneck crooklyn shanghai vida
auburn neosoul seoul bachata merengue
ride horses octavia butler macarons amor
old crow medicine
show
housewives of atlanta viet musica
grateful dead luther vandross kimchi
english and
spanish
mountain goats zora for my next meal espanol
love country music but waiting to exhale singapore salsa merengue
gillian welch anthony hamilton malaysian todo
country girl chrisette hk por
christmas vacation locs malaysia mariachi
bill bryson outside my race noodle soup marc anthony
riding horses kem cambodian espa ol
eric church octavia norwegian wood novelas
barn
real housewives of
atlanta
hong kong como
allman calypso
chungking
express
pero
willie nelson know why the caged rachmaninoff venezuela
harley did i get married southeast asia soledad
brunette spike lee vienna mas
flogging molly braxton mandarin tacuba
I discovered in the course of working with it that the algorithm we used to
make these lists is flexible. You can just as easily run the math in reverse. This
gives you the antitheses of a group—the stuff they especially don’t talk about—
which can be as illuminating as what they especially do. Here are the lists for the
men; they are printed on a darker background to visually emphasize that these
lists are the opposite of the previous ones. They are the words least used by these
groups yet most used by everyone else, the negative space in our verbal
Rorschach. The lists are worth reading all the way through:
most antithetical words for …
white men black men Asian men Latinos
slow jams borges sence southern accent
trey songz social distortion layed from the midwest
robin thicke tallest man on earth layed back ann arbor
smh gaslight anthem sence of humor midwestern
musiq snorkeling truck driver gumbo
merengue belle and sebastian 6′4 freakanomics
laker xkcd realy equity
ig diet coke anything else you wanna discworld
kevin hart surfboard like what u see shanghai
raised in nyc totoro and my son scallops
hip hop rap rb magnetic fields u like what u slopes
kpop gogol bordello care of my kids
university of
michigan
george lopez dropkick murphys makeing assessment
neo soul rebelution welder parentheses
rb and hip
hop
peru hunting fishing snowboarder
neyo
horrible’s sing along
blog
care of my son nyt
knw wakeboarding
wanna know anything
else
dominion
gud herzog else you wanna know msu
follow me my blue eyes raising my son ellipses
jordans guitar and sing ask and ill maple
handball dr horrible’s sing along comedys nigerian
soulchild coachella dnt kenya
ne yo dr horrible’s sing woman who wants john irving
bachata yo la tengo i’m a single father over a decade
basketball airborne toxic event somthing cheesesteaks
paid in full yosemite careing wall street journal
paid in full yosemite careing wall street journal
mos def talib feynman writting alternatively
mangas coppola and my daughter mistborn
abt wind up bird haveing weber
utada kar brown hair gravitate toward
The opposite-of-Latino list I found most surprising. Hispanic and white
identities are often conflated by demographers; for example, the US Census has
struggled for years to separate one from the other. But they can only use
checkboxes on paper. Latinos’ “most typical” list above and their “opposite” one
here define the extremes. That first gives you the furthest reaches of Latin
culture (music and language) and this second gives the “corn-fed” Midwestern
white stereotype, which is one of the few white subcultures with no Latin
influence. Also, please notice that the “least Asian” things are all misspellings,
working-class occupations, and other underachievements, like single fatherhood.
And of course there’s “64.”
The women’s lists are equally rich, and I again suggest you take in every
word. There’s the awesome my name is Ashley in the Asian antitheses. And I
have to say, as a point of professional pride—when you ask an algorithm “What
aren’t black women talking about” and it tells you “tanning,” you know you did
something right.
most antithetical words for …
white women black women Asian women Latinas
filipino belle and sebastian bbw midwestern
neo soul tanning god my children cincinnati
musiq bruins single mother of two classically
slow jams tahoe grandson kenya
rich dad poor dad
simon and
garfunkel
god my daughter neal
corinne bailey rae magnetic fields mother of three shanghai
bailey rae sf giants human services financial services
salsa bachata flogging molly
degree in criminal
justice
classically trained
aaliyah head and the heart single mom of two southern belle
jpop dodgers notice my eyes and cutting for stone
jpop dodgers notice my eyes and cutting for stone
smh wavy wanna know just ask in new england
salsa merengue naked and famous mexican and chinese antarctica
nujabes social distortion they are my world kavalier
48 laws of power mountain biking being the best mom full disclosure
musiq soulchild portugal. the man raising my children gravitate toward
neyo camera obscura a better life for brussels
2ne1 rancid associates degree in toronto
esperanza yo la tengo curly hair and march madness
mangas paddle boarding madea cambridge
zane armin im a single mom adventures of kavalier
n.e.r.d santa cruz mexican and italian food creole
coldest winter
ever
ecuador i’m a country girl meetup
mines ccr ellen hopkins parentheses
ratchet the dog park people notice my eyes arbor
aventura bbqing my name is ashley curl up with a
malcolm x origami brittany for my next meal
asians handshake at a daycare singer songwriters
carne gabriela my family my cell ann arbor
hw line is it anyway want a man that raleigh
earphones sunblock me and my son
interpreter of
maladies
I’ve talked about race a lot so far, and I’ve done so, as I’ve said, because it’s
something rarely addressed analytically. And the data I have is ideal for tackling
taboos. But sex is the single most important grouping that humanity has. It’s
existed forever, even stretching back to when we were just one people, and
perhaps because of those deep-time roots, gender roles are more universal and
more stubborn than any other. It’s easy to forget, given how ineradicable the
color line can seem, that ideas of race are a product of time and place. The Irish
and eastern Europeans weren’t considered “white” until the 1900s; in Mexico,
the indigenous Mayans and the mestizos with Spanish blood have been distinct
ethnic groups (and political opponents) for centuries. Yet to most people from
the United States, they’re both just “Hispanic.” But sexual division is a given in
human culture—every culture, every time.
Paradoxically, OkCupid isn’t the best place to explore the differences between
men and women, at least through the method we’ve developed here. Your sex is
built into how you use a dating site, so, for example, the most salient thing you
find about (straight) women from their profile text is that they’re looking for
men, and so on. Sex and profile text are inextricable, and analysis gets you little
more than tautologies. The ideal source for analyzing gender difference is
instead one where a user’s gender is nominally irrelevant, where it doesn’t
matter if the person is a man or woman. I chose Twitter as that neutral ground.
The lists below were made using the same math as the OkCupid lists above, but
they use the text from users’ tweets.
most typical words for …
men women
good bro my nails done
ps4 my sissy
james harden mani pedi
mark sanchez my makeup
my beard my purse
cp3 girls night
in 2k my hair for
bynum prom dress
the squad girls day
bro we retail therapy
manziel thanks girl
in nba my future husband
year deal to dye
iverson dress shopping
yeah bro too girl
kyrie happy girl
hoopin bobby pins
free agent wanelo
tim duncan my boyfriend and
scorer my belly button
offseason my roomie
hof girlies
xbox one dying my
david stern cute texts
yds girl crush
fantasy team my boyfriends
gameplay eyebrows done
gameplay eyebrows done
gasol curl my
lbj my hubby
bro u us girls
This gives you the distilled essences of men and women—read and grow
stupider. Remember, before you get depressed, that the method is designed to
find what’s unique about each group, find the things they don’t have in common
and bring them to the fore. It’s the mathematical version of the guy at the state
fair: caricature by algorithm instead of airbrush.
These are the words at the extremes, but for men and women, as for the ethnic
groups before, the essential vocabulary (“the,” “pizza,” and so on) is shared. In
fact, there’s a growing consensus among psychologists that men and women are
fundamentally very similar, despite the popular cosmology that has them on
different planets. Researchers at the University of Rochester recently
pronounced “Men Are from Mars Earth, Women Are from Venus Earth,”
concluding:
From empathy and sexuality to science inclination and extroversion,
statistical analysis of 122 different characteristics involving 13,301
individuals shows that men and women, by and large, do not fall into
different groups.
And yet, though my method is built to tease out differences, it’s hard to
imagine two more opposite sets of interests than the ones listed above. I can’t
tell which side to root for here—on the one hand, it’s surely a worse world
where women fixate on their appearance and men live the beef jerky lifestyle.
On the other hand, if men and women were exactly alike, life wouldn’t be much
fun. Same goes for the by-race lists above. Cultural differences, even if they’re
occasionally laughable, make the world a richer place.
The Mars/Venus thing, metaphor though it is, reminds me that the heavens are
an ancient reference point for science. Aristotle looked to the emptiness
overhead to verify his aether. Newton confirmed his law of inverse squares
through the motion of Mars. Even Einstein wasn’t truly Einstein until the sun
and moon said so, in a 1919 eclipse that confirmed the theory of General
Relativity. Even though we’re working on nothing so grand as all that here, I
have to say I hope that paper’s snarky strikeout typeface is premature, at least for
the things we like and talk about and the ways we spend our time. Look at it this
way: if there were no planet out there but Earth, it would be a very boring
universe.
1 Another, much more famous, example is: e
πi + 1 = 0. Here, astoundingly, the five most important values
in mathematics form a single equation. It’s called the Euler Identity, by the way. He was a slacker.
2 This example is adapted from “Zipf’s Law and Vocabulary,” by C. Joseph Sorell, Victoria University of
Wellington. Like any empirical law, Zipf’s is a very good (and time-tested) descriptive framework, but as
you can see there is some variance in observed outcomes. It’s like knowing that a fair coin comes up heads
half the time. Nonetheless, even after a thousand flips, it’s very unlikely that exactly half of them will have
been heads.
3 The algorithm converted all words to lowercase and so I present them like that here.
11.
Ever Fallen in Love?
A few years ago a couple of MIT students, as a class project, used
Facebook’s data to create a working “gaydar.” It was a simple piece of software
that behaved a lot like any human trying to make an educated guess about
somebody: it looked at who the person’s friends were. The program quickly
learned to recognize that a certain balance of gays and straights in a guy’s social
circle reliably indicated his sexuality; it didn’t need to know anything directly
about him at all. As the Boston Globe put it at the time, “People may be
effectively ‘outing’ themselves just by the virtual company they keep.” After the
students had trained it on known profiles, the software was able to correctly
predict if a man was gay 78 percent of the time, just from the nature of his social
graph. That’s a highly robust result when you consider that the expected success
rate, if the program were just guessing blind, would’ve been only … uh,
like … 10 percent? 2 percent? 8? π/2?
That’s just the thing—part of the reason the kids made a program to guess in
the first place—nobody really knows how many gay people there are. Past
estimates vary wildly, as past estimates are wont to do.1 The Kinsey Report in
1948 was one of the first scientific attempts to get a real number; it drew many
brows together over horn-rimmed glasses by suggesting that 10 percent of men
and 6 percent of women were gay. Later studies, many politically motivated and
all using either survey data or contrived setups in laboratories, have put the
number as low as 1 percent and as high as 15.2 We are now able to get a better
guess by a different route, and improving the accuracy here is important because,
as one study blandly put it, “This work can usefully inform public policy.” All
but four presidential elections since 1952 would’ve flipped had 5 percent of the
electorate changed their minds, so the question of whether a group makes up 1
percent or 5 percent or 10 percent of the country is of primal interest to the
political calculus. Although the number of gay people carries no moral weight—
even if there were just one in the whole United States, he or she would deserve
the same rights as everyone else—it’s a simple practical reality that policy
decisions depend on the actual size of the population.
Also, for a group historically so stigmatized, a well-supported number speaks
up where the individual cannot. It says: I am here. Gay people are a somewhat
unusual minority, in that they can seem straight, at least superficially, if they
decide they must. This surely involves a painful choice between selfpreservation and self-expression that few other people ever have to weigh. But
aside from the clear cost to the individual, “the closet” costs our society, too, as
secrecy allows old attitudes to go unchallenged—and prejudice unchallenged is
prejudice perpetuated. By forcing people to hide, intolerance creates its own
cynical logic: when a large portion of a group goes unrecognized, it only makes
marginalizing the whole easier. Visibility, on the other hand, creates acceptance.
Even at lower estimates, homosexuality is no more unusual than naturally blond
hair—which something like 2 percent of humanity is born with. In fact, being
gay appears to be much more common than that. It’s just less accepted and
therefore much more often forced from view. Think about that the next time you
pick up a celebrity magazine.
Turning to the data, Google Trends again shows its power to reveal what
people feel they cannot say. According to Stephens-Davidowitz, the Google
researcher, 5 percent of searches for porn in the United States are looking for
what he calls “depictions of gay men”—that’s a catchall that includes
straightforward queries like “gay porn” and related searches like “rocket tube,” a
popular gay portal. What’s more, that 1 in 20 ratio is consistent from state to
state, meaning that same-sex desire is unaffected by a man’s political and
religious milieu. This evenness has a few powerful implications. First, it
frustrates the argument that homosexuality is anything but genetic. If men from
such different environments as Mississippi and Massachusetts are looking for
gay porn at equal rates, that’s strong evidence that supposed external forces have
little effect on same-sex attraction.
The second implication of the state-by-state sameness in the data—that is,
what it reveals not so much about gay people but about intolerance—needs a
little time to unfurl. In early 2013, when he was still covering politics for the
Times, Nate Silver applied his famous poll-modeling technique to same-sexmarriage ballot initiatives across the country. As he had done in the presidential
elections, he aggregated data to get a snapshot of public opinion in each state,
and then he performed some forward-looking analysis to guess how those
attitudes might evolve. Silver estimated that gay marriage will be legal in fortyfour states by 2020.
An interesting thing about Silver’s work on the question, which was based on
political polls, is how it relates to another data source: what people in each state
told Gallup about their own sexuality. Here are those self-reported numbers
graphed against Silver’s most current projections for the acceptance of gay
marriage, state-by-state. I’ve coded each state by its legal treatment of gay
marriage and labeled a few of the outliers, as well.
On the horizontal, you see that, per Silver, Mississippi is the least tolerant
state and Rhode Island is the most. On the vertical axis, Gallup’s numbers range
from 1.7 percent in North Dakota, to 5.1 percent in Hawaii. And, as you see
from the slant of the trend line, the more accepting a state is of homosexuality,
the higher its self-reported gay population. Remarkably, if you walk that dotted
line out to 100 percent support of gay marriage (statistically imagining a future
world of perfect tolerance), you find it implies that roughly 5 percent of the
population would say they are gay, absent social pressure not to be. That’s the
same number implied by Google Search, where the lack of social pressure isn’t
just theoretical.
Furthermore, that trend line isn’t a function of folks simply living where
they’re more welcome. The state-to-state steadiness in searches for gay porn
provides evidence of this and so does mobility data from Facebook. Comparing
the hometowns of gay users to their current residences you find that relocation
explains only a small fraction of the variance in Gallup’s rates of homosexuality
above. Gay people do not disproportionately move to more tolerant places. On
the one hand, this is a testament to the strength of home ties, upbringing, and
simple inertia. On the other, it means that for every person picking up and
moving to a San Francisco or a New York City to live life fully, there are likely
dozens still living in self-negation.
If you accept these two independent estimates of 5 percent, arrived at using
three of the biggest forces in modern data—Nate Silver, Google, and Facebook,
with an assist from that standby of old-school polling, Gallup—you begin to see
those self-reported numbers in a different light. When Gallup tells us that, for
example, 1.7 percent of North Dakotans are gay, then perhaps something like 3.3
percent of the state is gay and unwilling to acknowledge it. In New York, about
4 percent of the population is openly gay, leaving maybe 1 percent gay and
silent. And likewise for every state. Against the steadiness of the data, the ups
and downs in self-reported gay populations take on a new meaning: it shows a
nation of Americans leading secret lives. This adds specific wisdom to the broad
poetry often attributed to Thoreau: “most men lead lives of quiet desperation and
go to the grave with the song still in them.” These are refugees of the soul, and
we see it in the data.
Data even gives us a picture of the collateral damage. Here’s StephensDavidowitz again:
In the United States, of all Google searches that begin “Is my husband …,”
the most common word to follow is “gay.” “Gay” is 10 percent more
common in such searches than the second-place word, “cheating.” It is 8
times more common than “an alcoholic” and 10 times more common than
“depressed.”
And those questioning searches are most common where repression is at its
highest: South Carolina and Louisiana, for example, have the highest rates, and
acceptance of gay marriage is below the national average in 21 of the 25 states
where this search is most frequent. One wonders what the people so intent on
driving homosexuality underground (or “curing” it) make of this data, and of the
sexless marriages and children with unhappy parents their efforts so clearly
create. Again, this isn’t rhetoric—it’s numbers. The old economic “misery
index” is inflation + unemployment. I suggest the social version is the fraction of
the population living in places where they can’t be themselves. It’s a situation
that serves no end but suffering.3
Unfortunately, Google Search is ineffective for estimating the number of
lesbians in the country. The many straight men looking for women-with-women
porn garbles the data. However, we can see shadows of Silver’s acceptance
estimates in OkCupid’s data, with some interesting twists. I estimate that more
than a quarter of the country’s dating gay population used OkCupid in 2013.4
Gay online daters generally should be more open than average about their
sexuality—after all, they’re putting up profiles on a website. However,
recognizing that many people would rather not broadcast their sexual identity
Internet-wide, OkCupid gives its gay users the option to “hide” their profile from
everyone except other gay users. Fifty-nine percent of gay men and 53 percent of
gay women take advantage of the option. In this data too, the correlation
between a state’s tolerance and openness is visible, though more so for women,
whom I’ve plotted below.
After you get past questions of “outness,” gay users look a lot like everyone
else on OkCupid. In the match questions, the site’s gay users show the same
rates of drug use, racial prejudice, and horniness as the straights, and gays want
the same types of relationships. In fact, for sexual attitudes, if any group is an
outlier, it’s straight women. They’re comparative prudes: 6.1 percent of straight
men, 6.9 percent of gay men, and 7.0 percent of lesbians are on OkCupid
explicitly looking for casual sex. Only 0.8 percent of straight women are, which
probably says more about the taboo against sexual forwardness in (straight)
females than anything else.5
The number of reported lifetime sex partners among all four groups is
essentially the same. The median for gay men and straight women is four; for
lesbians and straight men it’s five, but just barely.6 If there is a significant
difference in sexual behavior, it’s at the extreme end: there we find a stereotype
partially fulfilled. Highly promiscuous gay men (the cohort reporting twenty-five
or more partners) outnumber their straight male counterparts 2 to 1. Funnily
enough, in sex, as in wealth and language, we have an inequality problem.
According to this data, the top 2 percent of gay men are having about 28 percent
of the total gay sex.
To see how identities are formed around the labels “gay” and “straight,” we
can apply the “word rank square” method from the last chapter to investigate
personal self-descriptions. As before, profile essays give us a sense of what
makes each group unique versus the others: what’s special about lesbians, what
makes gay men different from straight, and so on, and the method puts
everything in the users’ own words. The behavioral data above shows that how
we love isn’t all that different, but below we see that who we love, of course, is.
The math forces up the vocabulary most typical of each group:
most typical words for …
gay men gay women straight men straight women
first wives i am gay
knows what she
wants
honest man
velvet rage old lesbian i have no kids man to share
tales of the city i’m a lesbian treat a woman to meet a man
you’re a nice guy i am a lesbian care of herself a man who knows
anything on bravo femme side never been married care of himself
music madonna
attracted to women
who
daughter family meet a man who
music britney lesbian friends for a good woman find a man who
music britney lesbian friends for a good woman find a man who
ltr oriented are femme treat a lady who knows what he
romy and michelle’s butch femme good women meet a man
new guys lesbian movies my kids my family
man who knows
how
barefoot contessa single lesbian hello ladies a nice guy who
kathy griffin u haul type of girl honest guy
single gay butch but woman that can a man who has
the comeback are feminine real woman are a nice guy
hiv positive femme who my son family christian man
density of souls elena undone woman to share like a man who
modern family glee the butch my daughter family a guy who has
ab fab not butch intelligent woman man that knows
most gay movies imagine god my kids love jesus
muriel’s music brandi girl that i can a man who will
christopher rice walls could meet a woman who man that has
muriel’s wedding lesbian romance have no children true gentleman
other gay femme women son family you are a gentleman
flipping out debs with the right woman guy to share
find mr feminine women treat her nice guy who
guy to date you’re femme right lady like a guy who
sordid lives soft butch great woman a guy that can
stereotypical gay my future wife a woman who can christian woman
flight attendant hunter valentine nice woman for a good guy
are you there
vodka
lesbian looking i like a woman you’re a gentleman
As before, I’ll let you interpret the users’ words in detail, and I’ll just point
out a few general trends. The two straight lists are all single-mindedly concerned
with the person’s (potential) partner. Every last entry for straight women is
focused on the guy she’s looking for (I’m counting Jesus here; he’s single), and
the men’s only departure from talking about women is to note the presence or
absence of children. These lists together read like “Me Tarzan, you Jane” in long
form. Or maybe as adapted by Nicholas Sparks.
The lesbian list is more inward-looking, with more self-description, but it’s
still quite similar to the straight lists. Like straight women, lesbians are very
much typified by the relationship they’re looking for (you’re femme, my future
wife); they’re just using different words.
The gay male list is very different from the other three. It’s full of pop culture
and has comparatively few references to the user’s immediate person and family.
Anything on Bravo has to be the most spot-on generalization of all time. That
said, it’s interesting that gay men are the least sex- and sexual identity–focused
of all three groups. Or rather, they get their identity from something besides sex.
This method is, again, made to emphasize differences between the groups, but
other data shows that the boundaries are porous. One of the most intriguing
findings from OkCupid is the answer to this match question, asked only of the
site’s self-identified straight users.
Q: Have you ever had a sexual encounter with someone of the same sex?
women men
Yes, and I enjoyed myself. 22,308 26% 12,070 7%
Yes, and I didn’t enjoy myself. 6,153 7% 10,100 6%
No, but I would like to. 14,896 17% 7,632 5%
No, and I would never. 42,286 49% 137,455 82%
85,643 167,257
That is, 51 percent of women and 18 percent of men have had or would like to
have a same-sex experience. Those numbers are far higher than any plausible
estimate of the true gay population, so not only do we find that sexuality is more
fluid than the categories a website can accommodate, we see that sex with
someone of the same gender is relatively common, whether people consider it
part of their identity or not.
The above data is from users who chose “straight” when signing up, but in
that same pull-down menu OkCupid offers “bisexual” as an option. About 8
percent of women and 5 percent of men choose it. I have seen much frustration
among bisexuals both on OkCupid and elsewhere with the idea that bisexuality
is not a “real” orientation—that, for example, bisexual men are just gay men
who haven’t come to grips with it yet. Many gay people see bisexuality as a
hedge. A recent study by the University of Pittsburgh Graduate School of Public
Health puts it well, if a bit dryly: “Respondents who identified as gay or lesbian
responded significantly less positively toward bisexuality … indicating that even
within the sexual minority community, bisexuals face profound stigma.”
Gerulf Rieger of the University of Essex, working with psychologists from
Northwestern and Cornell, concluded in a 2005 paper that in terms of genital
reaction to stimulus, almost all self-reported bisexual men were gay, some were
straight, and very few were physically aroused by both sexes. He thus described
male bisexuality as a “style” of interpreting arousal rather than arousal itself.
Understandably, this infuriated the bisexual community; Rieger later revisited
the topic to conclude that male bisexuality might be “a matter of curiosity”—that
“interest in seeing others naked, observing someone else having sex, watching
pornographic movies, or taking part in sex orgies” explained the apparent
disconnect between bisexuals’ self-reported attraction to both sexes and their
observed physical attraction to only one. Their minds enjoyed all types of sex,
but their bodies were more discriminating.
On OkCupid we find support for the spirit of Rieger’s conclusions, if not his
vague terminology. The vast majority of bisexual men and women seeks
exclusively one sex or the other on the site. Below I’ve shown where the people
who identify themselves as bisexual actually send their messages.
To land in either of the “message only” swaths, a user had to send 95 percent
or more of his or her contacts to that sex, so the threshold there is quite high; this
isn’t an accounting trick. Only a fraction of the bisexual user base has any
significant contact with both sexes. Whatever the mechanism, Rieger’s claim
that self-reported bisexuality doesn’t reflect observed behavior appears correct in
this case. Interestingly, for men, messaging changes over time. In that change we
find plausible evidence for the hedge narrative: more than half of younger
bisexual men message only other men, and that percentage drops steadily until
the mid-thirties, at which point most of the male bisexual user base is messaging
only women. This is what you would expect to see if men interested in men stop
identifying themselves as bisexual as they get older and become more
comfortable with being called “gay.” But this question takes longitudinal data to
fully answer, which we don’t have yet.
That said, who we say we are and how we behave are two separate things, and
the latter shouldn’t automatically disqualify the former. People are ultimately
free to describe themselves however they choose, and demanding that their
labels fulfill a researcher’s (or a website’s) definition is pointless. Any
discrepancy is ultimately the label’s fault, anyhow—individuals love in whatever
way feels right to them, and sometimes the words to describe it have to catch up.
On Valentine’s Day 2014, for example, Facebook launched more than fifty
different gender options (allowing users to choose terms like transgender or
androgynous instead of male or female). Ellyn Ruthstrom, president of the
Bisexual Resource Center in Boston, was talking about orientation and Rieger’s
work, but could have been speaking to my data too, when she told the Times,
“This unfortunately reduces sexuality and relationships to just sexual
stimulation. Researchers want to fit bi attraction into a little box—you have to be
exactly the same, attracted to men and women, and you’re bisexual. That’s
nonsense. What I love is that people express their bisexuality in so many
different ways.”
We certainly find this varied expression when we look at the “typical” words
in the profile text of bisexual men on OkCupid. In the top thirty are bisexual,
pansexual, cross-dressing, and heteroflexible. In their antithetical list, you see
close with my family and really enjoy my job—markers perhaps of the loneliness
and disaffection that come from being an outsider, even among other outsiders.
Bisexuality for women is a bit different. It’s more mainstream—or at least the
version trafficked by the likes of Miley Cyrus is. Perhaps because marketers
know that “sex sells” and that stars need to push boundaries, a kind of gay-forpay lite is common in today’s pop culture. In Miley’s case—though of course I
don’t know for sure—it seems like a costume to sell records, no different from
Gene Simmons’s face paint. Similarly in costume, scammers targeting guys
online will often select bisexual as the identity for their fake accounts. On
Facebook, 58 percent of fake profiles are “female bisexuals” versus just 6
percent of non-fake. On OkCupid, the problem isn’t quite that pronounced, but
selecting bisexuality along with a few other key indicators guarantees you’ll get
special review from the site’s admins.
But even on our legitimate profiles, which is almost all of them, female
bisexuality and straight male fantasy are linked. You really pull this out of the
data when you look at the profile text: it’s mostly women inviting the world to
threesomes with their boyfriends or husbands.
most typical words for …
bisexual women
bi female
bisexual female
me and my husband
me and my man
my boyfriend is
hubby and
we are a couple
i am bisexual and
me and my boyfriend
fun couple
couple we
married couple
we are not looking
fun with me and
do have a boyfriend
my bf and
female to join
girl to join
another couple
bi woman
my boyfriend my
i am bi sexual
my hubby and
join me and my
female for
my boyfriend and i
we are looking to
a triad
no single
send us
If I could put this to a beat and get Pitbull to do the middle eight, it would go
straight to number one. That said, for all the crassness of sexual-identity-asbusiness-plan, it’s a hopeful sign when a minority identity is something the
mainstream thinks is worth co-opting instead of suppressing. Indeed, for
sexuality, we see that things are changing, and quickly. Devising the projections
we looked at above, Nate Silver clocked a marked change in American attitudes
in the last decade. Acceptance of gay marriage accelerated markedly in 2004—
and he determined, “One no longer needs to make optimistic assumptions to
conclude that same-sex marriage supporters will probably soon constitute a
national majority.”
Thus, it all comes back to counting, and the fraction is going our way. Though
people have been gay forever, in the late nineteenth century, people began to
“self-disclose” their homosexuality as a political act. The phrase “coming out”
was coined a few years later. Now, the goal of living and loving openly, which
gay men and women have sought for so long, is near realized. The change is
epitomized in the “out” celebrities, of course, but more so in the millions of
other people whose names I’ll never know but who have helped tick the metrics
of acceptance ever so slightly upward. The day is coming when pollsters can put
down their pens, scientists will turn their lenses another way, and enterprising
students can use their algorithms to calculate other things. The day is coming
when the world will be so open, no one will need to guess.
1 Please see a map of the world circa 1491 for more information.
2 Survey data is frequently polluted by outside factors, like how the researcher chooses to word the
questions or chooses to weigh sexual experience against sexual identification.
3 And the political, religious, and entertainment careers of the people who perpetuate it.
4 This is based on two assumptions: (1) that roughly 5 percent of the country is gay and (2) that, of the
Census-reported 93 million singles in the United States, half are actually dating.
The government counts everyone who’s not married as “single,” which is obviously problematic in
estimating the true single population, especially among gay people. In 2013, OkCupid recorded activity
from 650,000 distinct gay profiles, which, by this arithmetic, is 26.8 percent of the actively dating American
gay population. Some small fraction of the accounts are duplicates or “ghosts” (seldom used), but
nonetheless the site’s share of the country’s gay dating market is substantial. In this note, as everywhere in
this chapter, “gay” and “bisexual” users are counted separately, and this calculation does not include the
latter.
5 There are gay hookup apps specifically for casual sex: Grindr and Scruff are the best known services for
men. The straight analogue for these apps is Tinder. It’s proportionately as popular, perhaps more so.
Therefore, I don’t think selection bias (for long-term relationships) in OkCupid’s gay population is any
worse than in its straight population, though I do admit this is an impossible thing to know for sure.
6 Forty-nine percent of straight men and gay women have reported four or fewer partners.
12.
Know Your Place
When I was in junior high we had a long lunch period, and
since everyone was too grown up at that age to really play or enjoy themselves,
after the eating was over, we all just posted up outside the school and waited for
the bell to ring us back to class. In the first few days of seventh grade, we sorted
ourselves on the asphalt hardtop, and that arrangement, once set, hardly changed
in three years. From nearest the cafeteria door to farthest, the order I remember
is:
• ultra-coolest kids (mostly from the Heights, which was the wealthier part of
town)
• the generically preppy kids
• the college radio REM/Cure people (this was pre-indie rock)
• the skaters
• the heshers (what we called the metalhead stoner types, and anyone else for
whom glue was more than just an adhesive)
• me and my friends
• A BIG BROWN DUMPSTER
• exchange students and kids with learning disabilities
Obviously, this alignment was more than just random. The dumpster, god
bless it, created a natural gathering point for the untouchables, and from there
the +/− polarity of the student molecule took over. Given that at one end of the
line my people were playing pencil-pop and debating the merits of Teenage
Mutant Ninja Turtles, The Role Playing Game Not The TV Show Because The
TV Show Is For Kids, everyone else fell into place by fundamental force.
One of the beautiful things about digital data, besides its sheer volume, is that,
like the back lot at Pulaski Heights Junior High, it has both physical and social
dimensions. A piece of paper has two axes, space-time four. String theory
predicts that our physical existence requires somewhere between ten and twentysix dimensions. Our emotional universe surely has that many and more. And in
combining these spaces—our interior landscape with our external world—we
can portray existence with a new depth.
The way we’ve looked at people and interaction so far—connections, profile
text, ratings, and so on—has mostly ignored physical place, but websites and
smartphones are of course gathering ample location data. Tweets are geotagged
with latitude and longitude; Facebook asks for your hometown, your college
town, your current home; many apps know the very building you’re standing in.
Here we’re going to layer identity, emotion, behavior, and belief over our
physical spaces and see what new understandings emerge. We’ll look at how
location shapes a person, and how people have laid new borders over our old
earth.
The boundaries of many communities were created by fiat or accident—or
both. The United States and the USSR split Korea on the 38th parallel because
that line stood out on a map in an officer’s National Geographic. Earlier that
same month, Germany was divided into zones of occupation that reflected, more
than anything else, whose troops were standing where at the time. Many of our
own American states were created by royal charter or act of Congress, their
borders drawn by people who would never see the land in person. Absentee
mapmaking was and still is a much more pernicious problem in Africa, the
Indian subcontinent, the Middle East—and everywhere else the tread of Empire
has stamped the soil. Only very occasionally have maps been drawn to reflect
“the will of the people,” and even in those cases, as we’ve seen in Israel, which
began its modern history as, officially, the British Mandate for Palestine, the
question naturally becomes: which people, whose will.
For websites, political and natural borders are just another set of data points to
consider. When information—fluid, unbounded, abstract—is your currency, the
physical world with its many arbitrary limits is most often a nuisance. At
OkCupid, rivers are an endless irritant to the distance-matching algorithms.
Queens is both a half mile and a world away from Manhattan. Try explaining
that to a computer. The problem is that when a person is online, he or she is both
of the world and removed from it. But that duality also means we can remix our
physical spaces along new lines, ones perhaps more meaningful than those
drawn by plate tectonics or the dictates of some piece of parchment.
Here you see a plot of how Craigslist carves up the country—each region in
the map is the territory served by a separate classified list. One mapmaker called
it the “United States of Craigslist” but “united” feels to me like the wrong word
—this is a partition, and, within the whole, each little zone is its own petty
kingdom. It’s a Holy Roman Empire of old furniture.
Once we begin to graft content to the spaces, the map becomes more
interesting. Below is Craigslist’s empire again, but overlaid with the most
popular locations listed on the site’s many “Missed Connections” board, where a
lonelyheart might post something like:
Both of us boarded the uptown Q at 34th. You were wearing a peacoat and
your eyes had that Audrey Hepburn twinkle. We locked stares a few times;
if you read this email me.
That’s the Manhattanite’s version, at least. Portlandia most often makes eyes
on the bus. California flirts by the elliptical machines. But for much of the rest of
the country, the venue of longing is Walmart.
Now we’re getting to a place that a traditional cartographer can’t take us, that
no satellite can pick up. The above is a simple and goofy page from a new kind
of atlas: behavioral and physical terrain as one.
In the above examples, Craigslist defined its borders a priori, by picking the
markets they wanted to serve. Most websites collect location data rather than
project it, and from these we can create a truly alternate map of the world,
actually move the borders and contours to fit the human landscape. Years ago,
an enterprising hacker scraped data from Facebook and plotted the shared
connections of the 210 million profiles he’d gathered. From the data he saw, he
divided America into whimsical states defined by friendship rather than politics.
There were seven of them—Pacifica (the Pacific Northwest), Socalistan
(California), Mormonia, the Nomadic West, Greater Texas (which included
Arkansas, Oklahoma, and Louisiana), Dixie in the Southeast, and then, in a
bright green swath stretching from Minnesota down through Ohio and over to
the Atlantic covering all of New England, Stayathomia. My kind of country.
Since then, smartphones, each one with a tiny GPS pinging, have
revolutionized cartography. Matthew Zook, a geographer at the University of
Kentucky, has partnered with data scientists there to create what they call the
DOLLY Project (Digital OnLine Life and You)—it’s a searchable repository of
every geotagged tweet since December 2011, meaning Zook and his team have
compiled billions of interrelated sentiments, each with a latitude and longitude
attached. DOLLY is an incredibly versatile resource, the output of which is only
now being explored. For Zook, it’s already had a few highly personal
applications. In February 2012, his office in Lexington was shaken by an
earthquake, and he turned to the database to see the psychological aftershocks.
The map below shows the density of reaction on Twitter, plotted over the
physical epicenter of the fault. Here we see contours of surprise laid over the
shifting earth:
Zook discovered that the quake’s emotional epicenter was just northwest of
the seismic one, in Hazard, Kentucky, and as simple as it sounds, this kind of
finding is truly new. The Craigslist maps, for example, could’ve been made in
the 1970s—after all, the idea for the website’s “Missed Connections” section
was lifted from newspapers. So before the Internet, if you’d really wanted to,
you could’ve clipped a month’s worth of listings from the main daily in, say,
each of the country’s top 100 cities, logged the data, and gotten very close to
what we saw a few pages ago. Even the Facebook/Stayathomia redefinition was
theoretically possible decades ago, provided a research team had the resources to
interview millions of people in their homes and track down their stated
connections.
But Zook’s map shows people’s instantaneous reaction to an event that lasted
a split second. Surveying Kentuckians later, even with infinite effort, he couldn’t
have generated a true report—not only do emotions change in the remembering,
but media coverage and talk about the quake would’ve hopelessly polluted the
data. People with smartphones don’t make seismographs obsolete but Zook’s
plot reflects the “impact” of the earthquake in a much more direct way than the
old Richter scale. Knowing nothing else about a quake, if it were your job to
distribute aid to victims, the contours of the Twitter reaction would be a far
better guide than the traditional shockwaves around an epicenter model.1
Even though each one is transitory, tweets collected together can capture more
than ephemera. A demonstration of DOLLY’s power on YouTube shows it
tracking the Dutch holiday of Sint Maarten, a sort of Germanic Halloween where
children go door to door singing for candy. In the data, you see people
celebrating not only in the major population centers of the northern Netherlands,
as you’d expect, but also in Western Belgium—the tweets reconnect old Holland
to Flanders, its cultural cousin. Thus we watch an animated visualization of
GPS-enabled data points, and see shadows of the Habsburgs.
Given the power of what we can already see through software like DOLLY,
the lack of longitudinal data is especially painful. On today’s research corpus,
time often feels like a phantom limb. Twitter currently gives us so much of that
multidimensional promise: we have every emotion, we have every spot on the
globe, but we still have only a few years to work with. In Europe, where the
combination of geography, culture, and language has been so volatile over the
centuries, imagine being able to track the Alsace-Lorraine as it changed hands—
German, French, German, French—each government imposing its culture on the
people, as if the region were a house taking on coats of paint. Or imagine the
Caribbean basin in the late fifteenth century and being able to watch first the
soldiers, then their religion, then their language overwhelm the land, Arawak to
Aztec. To see the ebb and fracture of a culture over decades is what DOLLY was
built for. All it needs now is the decades themselves.2
Geocultural insights can be found in other sources, too, and though in most of
them you lose the immediacy of Twitter, you get a different kind of depth in its
place. When websites pose questions directly to their users, we have a chance
not only to refine borders but to show they don’t really exist as normally
conceived.
Below are one million answers to “Should burning the flag be illegal?”
collected by OkCupid. Here my mapping software drew no political or natural
boundaries, it just organized belief according to latitude and longitude. This is
truly a nation defined by its principles, or, as you can see, two nations: Urban
and Rural. You can even see where one encroaches on the other: the rural
communities up the Hudson River and in Northern California’s wine country,
built up with Big City money, have Big City opinions as well.
Similarly, and in support of the earlier Google Trends finding that
homosexuality is universal, we see that same-sex searches have no borders, no
state, no country. Below is a plot of gay porn downloads, by IP address, taken
from the largest torrent network, Pirate Bay. This map, too, is without any predrawn guides, and as opposed to the OkCupid plot above, its theme is solidarity:
from Edmonton and Calgary down to Monterrey and Chihuahua, this is just
where people live.
There are as many ways to draw maps as there are sources of data. We’ve
been slowly working our way up off the page, building a psychological
dimension—how we feel about the flag, porn—on top of our maps. But it’s
possible to go the other way: data can tie abstractions back down to earth. Take
cleanliness, again via OkCupid. This is how often people say they shower:
On the one hand, the broad trend merely reflects the weather: where it’s hot,
people shower more. But down in the details there are a pair of good stories. In
Jersey’s lightness, you can read the gym/tan/laundry grooming obsession of
Pauly D and the Situation—Jersey is much more fastidious than the surrounding
states. And in Vermont you find the opposite philosophy: the crunchiness is
more than just a stereotype. Vermont’s the most unwashed state overall, and
truly an outlier compared to its immediate neighbors. According to Google the
state animal is the Morgan Horse. It should be a white guy with dreads.
Politics, weather, Walmart, and certainly earthquakes all have a strong
connection to the physical world, but in some of our data we can begin to see an
exclusively inner geography. Take lust, which in theory, should have no state.
But here we see it does, and a surprising one:
This pattern comes up again and again on OkCupid—the north central and
west of the country is more sexually open, more sexually adventurous, and more
sexually aggressive. Up the Pacific Coast you’d perhaps expect such
unconventional attitudes, but for many of these red-meat states, it goes against
type. Politically, OkCupid’s users in, say, the Dakotas are as conservative as
their reputation. Their profile text isn’t much different from anyone else’s. For
all other indicators, the states should not be dark, but in the data we see a
mysterious sexual intensification. This unexpected pattern reveals a further
power in Internet data; we can now discover communities that transcend
geography, rather than reflect it.
This data above does not prove that the Mountain Time Zone is one big highplains makeout party. In fact, the explanation is rather banal: if you are looking
for people to have sex with in a place like Pierre, South Dakota, your local
options are limited. So you try a dating site to find what you want. It’s simple
selection bias in our data, but there’s meaning there: where people can’t find
satisfaction in person, they create alternative digital communities. On a dating
site, that means communities with similar sexual interests. On other sites with
more diverse aims, where the users aren’t just there to flirt in groups of two (and
occasionally three), you get something richer.
Reddit is the fulfillment of that earliest ambition of the Internet—to bring farflung people together to talk, debate, share, spread news, and laugh. To collapse
space and create personal closeness. It’s one of the most popular sites on the
web,3 and it rightly calls itself “The Front Page of the Internet”—a lot of the
ridiculous viral stuff you see on the big aggregator sites originates there. There’s
a video trending on the Huffington Post as I write this—no joke—with the
headline: “This Deer Thought No One Was Watching It Fart, Now the Whole
World Knows.” I promise you, Reddit was watching it fart first.
The odd thing is, for all its influence, Reddit doesn’t really do anything; there
are no apps, no games, no profiles to speak of. Their New York office is in a co–
working space and smaller than my bedroom. The site itself is just a raw list of
links submitted by the users, who vote, and comment, and comment on the
comments, and modify, and repost all day long, in what feels like the world’s
biggest group of friends sitting on the world’s longest couch. Few Redditors
know each other’s names, let alone ever meet in person, yet their bond is no less
close for being anonymous: a forty-year-old woman in the Bay Area was alone
the day before Thanksgiving 2011 and posted as much. Her thread received over
500 comments in just a few hours (including, of course, many invitations to the
next day’s dinner) and the post quickly broadened, completely ad hoc, to connect
Redditors in many other cities.
The site is self-organized into thousands of themed subreddits. Each of those
is user-created and -moderated, and each has its own devoted set of posters and
commenters. These are places where people have created true virtual
communities from nothing but wide open space. There’s gaming, technology,
music, nfl, alongside a lot of home-grown topics that you’ll only find on Reddit:
explainlikeimfive—an example post: “In Hinduism and Buddhism where
the dead get reincarnated, how do they account for population growth?”
iama—“IamA reporter covering NJ Gov. Chris Christie. AMA! [ask me
anything]”
todayilearned—“TIL that the town of Boring, Oregon has ‘paired up’ with
the town of Dull, Scotland to promote tourism in both places.”
askreddit—“Ex-smokers of Reddit, what ACTUALLY WORKED to get
you to successfully stop smoking?”
whowouldwin—“Superman Prime vs Superman w/infinity gauntlet”
On the next page I’ve plotted the two hundred most popular topics, and this is
something you could properly call “the United States of Reddit.” It’s a
geography like the Craigslist division we saw before—made, in fact, by a similar
algorithm—but instead of physical geography, it plots a geography of interests,
of the collective Reddit psyche. And it shows distinct yet connected
communities. The size of each state corresponds to the popularity of the topic,
and the software put “like with like,” according to cross-commenting between
subreddits.
As we did before when we encountered an unfamiliar way to present verbal
data, you should search out a few known terms to get a feel for how everything
fits together. For me, this was easy. My favorite game, Magic: The Gathering
(magicTCG), is correctly surrounded by its unfortunate natural friends
MensRights, whowouldwin, and mylittlepony. Similarly, many sports (nfl, nba,
formula1, and so on) are grouped at the bottom. Everything pokemon is clustered
over to the left. Britishproblems, along the right edge, is next to australia and
soccer. It also makes sense that the most popular subreddits are in the center—
that is, not too far from anything. The red tint corresponds to how tight-knit each
subreddit is. It shows the degree to which the people posting post only there. The
darker the red, the more isolated the thread. This whole thing is an abstraction,
but it shows how people can locate themselves by what they find interesting or
funny or important rather than where they happen to sleep at night. It’s a map of
one particular collective consciousness.

Benedict Anderson is a professor at Cornell University, and he wrote a book
that sat unopened on my bookshelf a long time. I was supposed to read it for a
college class and didn’t, but through all my moves over the years I’ve carried it
with me; it’s been a stowaway in every U-Haul. The book’s called Imagined
Communities, and I opened it recently because the title finally seemed
applicable. Anderson’s main topics are nationalism and nation-building and he
suggests that a nation “is imagined because the members of even the smallest
nation will never know most of their fellow-members, meet them, or even hear
of them, yet in the minds of each lives the image of their communion.” He was
writing in 1981, but he could have been talking about the Internet. I don’t know
if Reddit is a nation, but it’s got plenty of communion. And it’s interesting to see
another purely digital community define its burgeoning identity. Earlier we saw
the ancient rush to communal violence, as directed at Safiyyah, Natasha, and
Justine on Twitter. Here, on Reddit, we see a few of nationhood’s better angels:
belonging, sympathy, sharing.
I’ve lived now in Brooklyn for twelve years—Imagined Communities had
collected quite a bit of that New York City schmutz by the time I pulled it down
to read—but the first place that book ever went with me was Texas. Right after
school, I had been living with a few other guys, and one of them, Andrew
Bujalski, who’s now a director, decided to move to Austin because he loved
Dazed and Confused and Slackers. He was making a pilgrimage to find Richard
Linklater. The rest of us had no plan, so we just attached ourselves to his.
Of course picking up and moving like that is the privilege of twenty-two-yearolds with nothing better to do but chase someone else’s dream. We’d heard
Austin was cool, so we went there. It’s a lightweight example, but group
movements like this, based on little more than word of mouth and hope for
something better, created the world as we know it. The Great Migration—
millions of African Americans leaving the Jim Crow South for cities like
Detroit, Chicago, and New York in the early 1900s—was a transformative
cultural shift for the country and was made of thousands of small-scale pick-upand-move decisions. Same with the gold rush that settled California. Same with
much of the European settlement that brought the Old World to this continent in
the first place. Same with, I imagine, the bands of Clovis people who crossed the
ice bridge 13,000 years ago to become the very first nation on this soil.
Communities move to find an environment that will sustain them and where they
are safe, but also to find a physical place that reflects what they feel within.
Recently, Facebook’s Data Science team took a worldwide look at modern
large-scale movements—coordinated migrations, where a significant proportion
of the population of one place has moved, as a group, somewhere else. People
don’t move en masse like this in the United States much anymore, but in many
places, they’re just beginning to. The researchers plotted coordinated movements
around the globe. Here I’ve excerpted a small section of their map of Southeast
Asia: the lines show small towns and villages relocating wholesale to urban
centers. It’s a static picture of a rapidly changing region. For what it’s worth, this
could’ve been England circa 1850, or the United States fifty years later.
In the broadest sense, these moves are most likely driven by economics—
cities like Chicago or Bangkok promise jobs. But though the lines and dots on
this map are aggregates, the migrations they reflect are all small, personal, and,
no doubt, unique to the people making them. Was it a parent who made the
decision to pack up and go? Did a friend lead the way? Who did these people
join in their new city? Who did they leave behind in the old? Did they bring
everything? Leave everything? And I can’t help but wonder, too, does everyone
have a book that follows them until they read it? And, if so, what is theirs?
1 Two months later Zook measured a convulsion of another kind: the Kentucky Wildcats won the NCAA
championship and the students got wasted and burned shit like the future leaders they no doubt are.
#LexingtonPoliceScanner began trending as a hashtag, based mostly on this tweet from @TKoppe22: “Uh
We have a partially nude male with a propane tank #LexingtonPoliceScanner.” Zook tracked that tag to
show how formerly local nonsense can now reverberate worldwide. The highbrow/lowbrow schizophrenia
of Twitter never stops amazing me. It’s the Chris Farley of technologies.
2 I realize an added condition is that the affected people use Twitter, and that in the context of preColumbian Mesoamerica that’s an absurd expectation. However, as I’ve said before, the service is much
more pervasive and more democratic than most people think, and if anything similar to the Spanish
Conquest were to happen today, you most certainly would see the reverberations on Twitter.
3 In December 2013 it had 101 million unique visitors and served 5 billion pages.
13.
Our Brand Could Be Your Life
Bass Ale’s triangle logo was the first registered trademark in the
English-speaking world, and today that sturdy oldness is a big part of the brand’s
appeal. They lay it down right there on the label—“England’s first registered
trademark.” But what they don’t tell you is that Bass was only first because a
brewery employee happened to be first in the queue at the registrar’s office the
morning that Britain’s Trademark Registration Act took effect. They’ve parlayed
an accident of bureaucracy into a reputation that, at least judging by what’s in
those brown bottles today, far outstrips the actual quality of the product. Bass is
a brand built on nothing more than the act of branding itself.
There were many brands and marks before Bass—enough for the UK to begin
to regulate them, after all, and labels and image-making pre-date even the
Industrial Revolution. I mean, brands were originally burned into flesh. It’s hard
to get more primitive than that. Archaeologists have unearthed branded oils and
wine in desert tombs sealed five thousand years ago. One label found in Egypt
reads “finest oil of Tjehenu” beneath the royal emblem and a pictograph of a
golden oil press. Compare that to the “choicest hops, rice and best barley”
beneath the “King of Beers” on a can of Budweiser—as far as branding has
come, in many ways it will probably always be a Bronze Age science, because
the emotions it plays to are eternal.
But while aspiration and the prestige of association may be timeless concepts,
truly new territory has recently opened to the brand: people. In 1997, Tom
Peters, a motivational speaker and management consultant, published an article
called “The Brand Called You” in Fast Company magazine, and the era of
personal branding was born.
His article, really more of a sales pitch, asks readers to first determine their
“feature-benefit model” and then to relentlessly market it to employers,
coworkers, and the larger world … or else! Those are literally the last two
words, and they punctuate all the typical hokum (“Sit down and ask
yourself … what do I want to be famous for? That’s right—famous for!” and
“You are a leader. You’re leading You!”) that the worst business writing has to
offer. Reading it, you imagine Mr. Peters miked up and pacing the rostrum like a
lion caged—caged by that darn paradigm that he’s about to explode before your
very eyes, with truth bombs, know-how, and exclamation points. He shows the
kind of belief that a different type of person channels to rip phone books in half
for his tight bro J.C. The byline at the bottom of the piece reads, “Tom Peters is
the world’s leading brand when it comes to writing, speaking, or thinking about
the new economy.” He was also, at that point, not just the leading, but the only
person calling himself a brand. Hence a mouthpiece for the “new economy”
takes a page from Bass’s Victorian playbook. And why not? Fake it till you
make it. The article kicked off the idea of self-branding as a direct path to
success and is still read in marketing classes today.
A few years later, a man named Peter Montoya expanded upon Peters’s idea
in a second influential manifesto called The Brand Called You. Yes, it had the
same title as the original manifesto, and no, he and Mr. Peters did not work
together; in fact, if anything, the two men are rivals in the branding-guru
business. Melding the cold steel of cluelessness to brass balls is the well-paid
talent of pitchmen everywhere, and Mr. Montoya just might be the master
wizard. The Brand Called You (his version) is essentially one long outline, and
this is the very first bullet point, which appears on this page:
1. You Are Different. Differentiation—the ability to be seen as new and
original—is the most important aspect of Personal Branding.
Naturally, The Brand Called You, the remake, was a bestseller, and Montoya,
like Peters, has a thriving speaking career to this day. But if the pitch to be “your
own personal brand” had gone no further than the nation’s convention halls and
hotel ballrooms, just absorbed like so much cold coffee and muffin dribblings
into the tattered carpet of the zeitgeist, I wouldn’t be writing about it. The idea
had legs, strong ones, and now you see whenever there’s a public faux pas or a
stumble from grace by some national figure, the natural question is: How will it
affect his or her personal brand? Peters and Montoya were innovators, and I
mean that sincerely. Some of the smartest and most deservedly successful people
I know say the words “my brand” without irony. You can see the birth of the
idea and its subsequent rise through mentions in print via Google Books:
Of course, the principles of personal branding aren’t new. Neither Montoya
nor Peters
1 are all that different from Dale Carnegie, who rebranded himself
from the plain “Dale Carnagey” by borrowing the golden surname of the steel
magnate Andrew, and who, like these latter-day men, reduced character to bullet
points and saw influence above all as the key to success. The goals of personal
branding are the same you’d find in any empowerment seminar or in any
prosperity gospel sermon from any decade. The end has always been wealth and
power.
The new part is that “personal branding” asks you to accomplish these ends by
treating yourself like a product rather than a human being. Peters again:
Starting today you are a brand. You’re every bit as much a brand as Nike,
Coke, Pepsi, or the Body Shop. To start thinking like your own favorite
brand manager, ask yourself the same question the brand managers at Nike,
Coke, Pepsi, or the Body Shop ask themselves: What is it that my product
or service does that makes it different?
This is the core concept of personal branding, and like Christianity + the
printing press or pro football + television, the idea has found in social media the
perfect technology to go global. I won’t rehash the ways sites like Facebook,
Twitter, and Instagram give you the power to project yourself to the world. But I
will point out that not long ago, only big companies, with big budgets, could get
their message heard and beloved by strangers halfway around the globe. Now I
can, and so can you, and so can everyone. The hardest part is getting anyone to
listen.
The straightforward way is just to be entertaining, engaging, funny. But
there’s a reason comedians who can actually make people laugh are very rare.
It’s hard. An amateur who tries to build a following by being witty or
provocative on Twitter is far more likely to end up the next Justine Sacco than
the next Justin Halpern (@ShitMyDadSays), with his 3 million followers and a
book deal. For every kid who tweets herself into college or into a cool job at the
New Yorker—as people have done—there must be dozens who tweet themselves
into the principal’s office, or more likely, into a brick wall of embarrassed
silence.
You can see something of what it takes to build a following using our text
analysis algorithm. Here are the typical words for what I would call the “rank
amateur” and “budding professional” follower levels:
most typical words for …
people with <100 followers people with 1,000+ followers
#thehungergames partnering
#upset #heyboo
#worthit vamping
#whyme optimizing
roethlisberger sourcing
workaholics marketer
#wordsofwisdom tweetup
#hurryup visibility
#depressed monetize
#wishmeluck industry’s
#getonmylevel optimize
#studying brownskin
#idiots merchants
cincy influencers
#collegeproblems robust
#sunny yeen
#notokay guwop
#finalsweek talmbout
#tebow innovators
#silly partnered
#silly partnered
#impatient bezos
#leavemealone infographics
#holyshit livest
#suckstosuck strategist
pujols entrepreneurial
#saveme slideshare
#yeahbuddy yass
pattys amplify
#girlproblems goodmorning
#killme creatives
On the left you see the kinds of simple, fleeting concerns you’d expect from
people on Twitter. On the right you see almost entirely management jargon: if
you have a lot of followers, you are in fact much more likely to speak like a
corporation. But some words on the right aren’t typically professional: #heyboo,
talmbout (a contraction of “talking about”), yeen (“you ain’t”), yass (“your ass”),
and a few others. Those are people using Twitter just like the folks on the left—
to talk shit, complain, one-up—only they’re doing it in wider circles, to
thousands of followers. The users behind those words are black, and those terms’
presence on the right side of the list is evidence of the different way African
Americans tend to use the service. (I emphasize tend because no group is a
monolith.) Observers call the phenomenon Black Twitter, described here by
Farhad Manjoo in Slate:
Black people—specifically, young black people—do seem to use Twitter
differently from everyone else on the service. They form tighter clusters on
the network—they follow one another more readily, they retweet each other
more often, and more of their posts are @-replies—posts directed at other
users. It’s this behavior, intentional or not, that gives black people—and in
particular, black teenagers—the means to dominate the conversation on
Twitter.
By “dominate,” he’s referring to the fact that in Twitter’s early years there
was a lot of confusion from white users when hashtags like #uainthittinitright
and #ifsantawasblack would make the service’s Trending Topics list, alongside
the latest deep thought from Ryan Seacrest or marketing gimmick from Old
Spice (just as #heyboo might seem confusing alongside “monetize” above).
Most users on Twitter follow institutions of one kind or another (celebrities,
journalists, products) and those institutions don’t follow them back. The
mainstream culture of the service is organized around that one-to-many
communication, organized, in fact, around the brand. But black users tend to
focus on personal use and are highly reciprocal—hence high-follower counts and
the enhanced ability to launch memes to the top of the charts.
Anyone hoping to build their brand on the service in the mainstream way—to
become the one for the many—should realize that Twitter is very much the
world of the One Percent. Its most precious resource, followers, is distributed far
more unequally than wealth. In my sample, the top 1 percent of accounts has 72
percent of the followers. The top 0.1 percent has just over half. It is much, much
harder to get to a million followers than it is to make a million dollars. There
were 300,890 people who reported over $1 million in income to the IRS in 2011.
Right now there are 2,643 Twitter accounts with 1 million followers, worldwide.
Perhaps half are in the United States. Being an American with 1 million Twitter
followers is roughly equivalent to being a billionaire.2
Of course, that assumes the followers are real. I bought some for one of my
accounts to see how it works. On a site like TwitterWind, you can choose a
number from a menu (I chose 1,000), pay up ($17), and a day or two later, and
pretty much all at once, you get that many new, useless friends. The followersfor-hire do nothing at all but exist, and yet almost everyone with a really big
Twitter following has probably bought some—especially the people for whom
seeming popular is practically the whole job, like celebrities and politicians.
When the Republican nomination was still up in the air, Newt Gingrich boasted,
“I have six times as many Twitter followers as all the other candidates
combined.” The only catch was he’d paid for about 90 percent of them.3 Mitt
Romney (almost certainly) bought followers, too: for example, he gained 20,000
followers in a matter of minutes one day in July, which was about 200 times
what he was getting immediately before and immediately after. Now, please note
two important points: one, a person can buy followers for someone else, so this
very well might’ve been some twenty-first-century Nixon working his ratfucking
magic; it was certainly a good way to make Mitt look like a doofus. And, two,
I’m sure Obama and many, many Democrats have bought followers for
themselves. Craven attempts to game the system are a staple of both parties.
They’re just usually not as easy to catch as this:
You can understand why these guys do it. The more popular someone seems
to be, the more popular they become. It’s as close as you can get to buying votes,
at least until the Supreme Court makes that legal in 2018.
Everyday account holders are no less susceptible to the lure of easy friends,
even if they don’t have Barack’s or Mitt’s budget. Two of the five most common
hashtags in my randomized Twitter data set (coming in at number one and
number five, respectively) are #ff and #teamfollowback. The first stands for
“Follow Fridays,” which was an old-school tradition on Twitter—on Fridays you
would tweet out people you like for your followers to follow. It’s now just
general (any-time) shorthand for “hey follow these accounts,” and commonly
blasted out by users just trying to drive numbers. The second, #teamfollowback,
is the hashtag/handle for a Twitter account that basically does for free what
politicians can afford to pay for. The idea is you follow TeamFollowBack, and
the account’s other followers will follow you. You then, in turn, follow them
back, and everybody’s numbers have risen. It’s like the old idea of a “web ring,”
which in the days before Google was a way for websites to all link to one
another and ensure traffic. It’s also like the old idea of a full-on circle jerk.
Here’s TeamFollowBack’s self-description:
We will help you get followers that follow back! THE ORIGIONAL
[sic] & THE BEST - Promote OUR hashtags #WILLFOLLOWBACK
#TEAMFOLLOWBACK
So this is what the self-as-brand can lead to: chasing empty metrics. I know
when I tweet, I’m as interested in who shares it, and how quickly, as I am in
whatever I was originally trying to communicate. The few times I’ve posted to
Facebook I’ve sat there and refreshed the page to catch the new comments, as
though I’d never been on the Internet before. Jenna Wortham from the Times
describes this mentality well: “We, the users, the producers, the consumers—all
our manic energy, yearning to be noticed, recognized for an important
contribution to the conversation—are the problem. It is fueled by our own
increasing need for attention, validation, through likes, favorites, responses,
interactions. It is a feedback loop that can’t be closed, at least not for now.” I can
tell you from the inside: companies design their products to jam that loop open.
OkCupid shows you little counts of your messages, your visitors, your
possibilities. We know that those numbers keep our users interested, especially
when they go up. Without little bits of excitement, a webpage or an app seems
dead and people drift off. The broad term for this is “user engagement,” how
many people check in every week, every day, every hour. It’s basically how fast
they are running in the hamster wheel that’s been set down for them there in
cedar filings, and it’s one of the most obsessed-over measures in the industry.
Sites show you counts, totals, badges, because they know you’ll come back to
see them tick up. Then they can put your increased engagement on a slide to
impress their investors.
That’s the thing: it’s one thing to reduce yourself to a number. When someone
else reduces you, it feels ugly. Klout is one of the leading personal analytics
firms; they look at all your social media accounts and, through a little proprietary
black magic, give you an all-in measure of your online influence, 0 to 100.
You’ll remember per Montoya (and Carnegie): influence is what a personal
brand is all about, and Klout helps you figure out how you’re doing. Right now,
my Klout score is a fairly pathetic 34. TeamFollowBack comes in at 60, which
makes me want to either laugh or cry. On the one hand, these people have gotten
the equivalent of a D− grade on their only reason to exist. On the other, they
have a higher score than anyone I know.
In 2012, Salesforce.com, the cloud-computing behemoth, posted a job
opening that listed a Klout score of at least 35 as a “desired skill.” It wasn’t
positioned as a requirement, but they put it up there along with the allow-us-to-
state-the-obvious attributes like “ability to work … as a part of a team,” so it was
presumably a core part of the job. Salesforce’s business specialty is
quantification—they help companies market through data.4 So it’s not that
surprising that they would approach hiring in the same quantified way. But even
though numbers like credit scores have been an odious part of the HR process
for some time, seeing a Klout score on a job listing got a lot of people upset.
BetaBeat’s article “Want to Work at Salesforce? Better Have a Klout Score of
35 or Higher” got the general reaction just right with their one-word subhead:
“Ugh.” However, the real concern: that we’re all going to be reduced to
numbers, and soon, deserves a longer discussion. Salesforce was, and is, a
trendsetter—certainly in the world of online marketing. They were Forbes’s
“Most Innovative Company in America” the same year they put up that post.
They hire hundreds of people a year, and, even more to the point, when awardwinning innovators do something new, other companies copy it. If Salesforce is
asking for Klout scores, then everyone will soon be asking for Klout scores.
People don’t want to be reduced to a two-digit number, concocted by a company
that even in the vaporous world of social media startups seems kind of bullshitty.
But given that Klout uses many of the same reductive tools that I myself have
employed to gather data, where does that leave you and me and the book we’ve
both spent all this time with? Well, the short answer is: right there with Klout
and Salesforce. Reduction is inescapable. Algorithms are crude. Computers are
machines. Data science is trying to make digital sense of an analog world. It’s a
by-product of the basic physical nature of the microchip: a chip is just a
sequence of tiny gates. Not in the way that the Internet is a “series of tubes” but
in actuality. The gates open and close to let electrons through, and when one of
these gates wants to know what state to be in, it’s all or nothing—like any door,
a circuit is open or it isn’t; there are no shades of maybe. From that microscopic
reality an absolutism propagates up through the whole enterprise, until at the
highest level you have the definitions, data types, and classes essential to
programming languages like C and JavaScript.
Thus, information is reduced by necessity. But fundamentally the objections
to the Klout-score requirement were about the people being reduced to digits,
not just their information. And here’s where Dataclysm diverges from
Salesforce’s job post, and indeed Klout’s whole business model.
As many numbers as there are here, they’re not meant to stand in for any one
person. A single number never could. It’s a truth summed up by the apocryphal
story that Einstein flunked math in high school. He didn’t. But he could’ve, and
if he had, who cares? If he got a 35 in Algebra II, so what? Is he suddenly not
smart? No number, no test, no single measurement—not IQ, not height, and
certainly not a Klout score or friend count or reply percentage on OkCupid—is a
whole person, which is exactly why, beyond illustration, individual users don’t
appear in this book. But by aggregating a bunch of these small and inadequate
parts of us together, we get something big. The law of large numbers is an idea
we’ve brushed past a few times, but I want to lay it out explicitly: the full truth
of data is only revealed over a large sample. Imagine a mysterious die—you
can’t count the sides but you can roll it and see what comes up. Roll once and
you could get any number, you learn nothing. Roll it a bunch of times, you get
the distribution, you get the average—and that defines the die right there. You
know the shape only through aggregation.
What’s more, reduction and repetition are fundamental to the long history of
science, not just data science and not just computer science, but capital-S
Science, the ageless human enterprise. Experiments are built upon reducing a
process to a single, manageable facet. The scientific method needs a control, and
you can’t get it without cutting complexity to the bald core and saying this, this,
is what matters. Only once you’ve simplified the question can you test it over
and over again. Whether at a lab bench or a laptop, most of the knowledge we
possess was acquired like this, by reduction.
So here, we’ve boiled humanity down to numbers rather than, say, anecdotes.
In my mind—and this takes nothing away from Malcolm Gladwell—I see this
book as the opposite of outliers. Instead of the strays from the far reaches of the
data—the one-offs, the exceptions, the singletons, the Einsteins for whom you
need the whole story to get it right, I’m pulling from the undifferentiated whole.
We focus on the dense clusters, the centers of mass, the data duplicated over and
over by the repetition and commonality of our human experience. It’s science as
pointillism. Those dots may be one fractional part of you, but the whole is us.
Aggregation and reduction also allow us to deal in broad trends, the smooth
flow of which might not have the peaks and troughs of the usual hero narratives
but which are all the more applicable for it. The fact that Paul McCartney and
John Lennon practiced rock music for 10,000 hours and then became the Beatles
does say something about the value of rehearsal and persistence, but that number
itself means nothing. I myself have put in that kind of time playing guitar, as
have many others whose music you’ll never hear. Whatever it was that allowed
Lennon and McCartney to turn practice into genius, it’s unique to them. On the
other hand, every number in this book has many hundreds, often many
thousands, of people behind it, none of them famous. Here’s the kernel of it: the
phrase “one in a million” is at the core of so many wonderful works of art. It
means a person so special, so talented, so something that they’re practically
unique, and that very rareness makes them significant. But in mathematics, and
so with data, and so here in this book, the phrase means just the opposite:
1/1,000,000 is a rounding error.
But if simplifying is what it takes to understand large data sets, I do worry
about a different kind of reductionism: people becoming not a number exactly,
but a dehumanized userid fed into the grind of a marketing algorithm; grist for
someone else’s brand. Data takes too much of the guesswork out of the sell. It’s
a rare urban legend that turns out to be true, but Target, by analyzing a
customer’s purchases, really did know she was pregnant before she’d told
anyone. The hitch was that she was a teenager, and they’d started sending
maternity ads to her father’s house.
In some ways, that kind of corporate intrusion is better than brands actually
trying to “relate.” Last summer, a Jell-O marketing campaign co-opted
(tagjacked?) the hashtag #fml, which is Internet shorthand for “fuck my life.”
Their social media people began responding to tweets that contained the tag with
an unsolicited offer to “fun” the person’s life instead, with coupons. Thus people
in extremis received jaunty offers from a gelatin, as in this exchange:
Pyrrhus Nelson @suhrryp
Seeing my bank account disappear at the dr office #fml
JELL-O @JELLO
@suhrryp Fun My Life? Of course we will. In fact, we’d be happy
to.
prmtns.co/dkTq Exp. 48hrs
This kind of unwanted intercession is all too easy on social media because
everything is so quantified. The hashtags jump right to the brand manager’s
screen; he dives in with the discounts. At least the same technology that allows
them into our lives allows us to fight back. A few years ago, McDonald’s sent
out a couple tweets, feel-good stories about their suppliers, with the tag
#McDStories, and they got #fml’d in reverse. This is just one of many responses:
MUZZAFUZZA @Muzzafuzza
I haven’t been to McDonalds in years, because I’d rather eat my
own diarrhea. #McDStories
McDonald’s had paid to promote the hashtag and pulled the campaign after
only a couple hours when it quickly spiraled out of their control. A week later,
the repurposed #McDStories was still going strong. Their social media
strategists should’ve known what to expect: a few months before, Wendy’s had
tried to push #HeresTheBeef, and their catchphrase was ripped completely free
of the intended context. People used it to complain about anything they didn’t
like (had a beef with), ignoring the brand:
Remi Mitchison @RemiBee
#HeresTheBeef when a chick see another chick doin better and
has more than she does … so she wanna stunt and
#GetThatAssBeatUp
Jeremy Baumhower @jeremytheproduc
#HeresTheBeef The drugs companies have already cured HIV
and cancer, however it is far more profitable to keep people
barely alive on drugs
More recently, Mountain Dew ran a “Dub the Dew” contest, trying to ride the
“crowdsourcing” wave to a cool new soda name and thinking maybe, if
everything went just right and the metrics showed enough traction to get buy-in
from the right influencers, they’d earn some brand ambassadors in the
blogosphere. Reddit and 4chan got ahold of it, and “Hitler did nothing wrong”
led the voting for a while, until at the last minute “Diabeetus” swooped in and
the people’s voice was heard: Dub yourself, motherfucker.
The Internet can be a deranged place, but it’s that potential for the unexpected,
even the insane, that so often redeems it. I can’t imagine anything worse for
You! The Brand! than upvoting Hitler. Plus, what a waste of time, because
obviously Mountain Dew isn’t going to print a single unflattering word in the
style of its precious and distinctive marks. I find comfort in the silliness, in the
frivolity, even in the stupidity. Trolling a soda is something no formula would
ever recommend. It’s no industry best practice. And it’s evidence that as much
as corporatism might invade our newsfeeds, our photostreams, our walls, and
even, as some would hope, our very souls, a small part of us is still beyond
reach. That’s what I always want to remember: it’s not numbers that will deny us
our humanity; it’s the calculated decision to stop being human.
1 His mantra, by the way, is “distinct … or extinct.”
2 The 2014 Forbes Billionaires list has 1,645 members.
3 One of Newt’s former staffers told Gawker: “About 80 percent of those accounts are inactive or are
dummy accounts created by various ‘follow agencies,’ another 10 percent are real people who are part of a
network of folks who follow others back and are paying for followers themselves (Newt’s profile just
happens to be a part of these networks because he uses them, although he doesn’t follow back), and the
remaining 10 percent may, in fact, be real, sentient people who happen to like Newt Gingrich.”
4 As an analytics bona fide, they even own data.com.
14.
Breadcrumbs
Facebook released the Like button in 2009 and it changed
the way people shared content. The idea wasn’t new—once-popular, now
marginal, sites like digg.com and del.icio.us had been letting people “like”
articles for years before that. But at these companies, the content was the star.
Facebook laid curation over an already robust social network and, for the content
creators, made it simple for anyone to attach that iconic little thumbs-up to their
work. They created a new universal microcurrency—I might not pay you for
your writing, music, or whatever, but I’ll give you a fillip of approval and share
what you’ve done with my friends. As of May 2013, Facebook was recording
4.5 billion likes a day and in September of that year reported that 1.13 trillion
had been submitted all-time.
Those students from MIT developed their gaydar the same year likes
launched. Their algorithm was pretty good at guessing a man’s sexuality, but it
also worked in a fairly obvious way: it’s surely no big secret that gay men are
more likely to have gay male friends. The gaydar innovation was to use macrolevel data to do something people had been doing in small ways all along. Since
then, the power of predictive software has advanced rapidly; these types of
programs only get smarter and faster as more data becomes available. By 2012, a
group from the UK had discovered that from a person’s likes alone they could
figure out the following, with these degrees of accuracy:
whether someone is …
gay or straight 88%
lesbian or straight 75%
Caucasian or African American 95%
a man or a woman 93%
Democrat or Republican 85%
a drug user 65%
the child of parents who got divorced before he or she turned 21 60%
Again, this is not from looking at status updates or comments or shares or
anything that the users typed. Just their likes. You know the science is headed to
undiscovered country when someone can hear your parents fighting in the clickclick-click of a mouse. A person’s “like” pattern even makes a decent proxy for
intelligence—this model could reliably predict someone’s score on a standard
(separately administered) IQ test, without the person answering a single direct
question.
This stuff was computed from three years of data collected from people who
joined Facebook after decades of being on Earth without it. What will be
possible when someone’s been using these services since she was a child? That’s
the darker side of the longitudinal data that I’m otherwise so excited about. Tests
like Myers-Briggs and Stanford-Binet have long been used by employers,
schools, the military. You sit down, do your best, and they sort you. For the most
part, you’ve opted in. But it’s increasingly the case that you’re taking these tests
just by living your life. And the results are there for anyone to read and judge.
It’s one thing to see that someone’s Klout score is 51 or whatever in advance of
a job interview. It’s another to know his IQ.
If employers begin to use algorithms to infer how intelligent you are or
whether you use drugs, then your only choice will be to game the system—or, to
borrow the wording from the previous chapter, “manage your brand.” To beat
the machine, you must act like a machine, which means you’ve lost to the
machine. And that’s all assuming you can guess at what you’re supposed to do
in the first place. Apparently, one of the strongest correlates to intelligence in the
research was liking “curly fries.” Who could reverse-engineer that?
But while Facebook does know a lot about you, it’s more like a “work
friend”—for all the time you spend together, there are clear limits to your
relationship. Facebook only knows what you do on Facebook. There are many
places with much deeper reach. If you have an iPhone, Apple could have your
address book, your calendar, your photos, your texts, all the music you listen to,
all the places you go—and even how many steps it took to get there, since
phones have a little gyroscope in them. Don’t have an iPhone? Then replace
“Apple” with Google or Samsung or Verizon. Wear a FuelBand? Nike knows
how well you sleep. An Xbox One? Microsoft knows your heart rate.1 A credit
card? Buy something at a retailer, and your PII (personally identifiable
information) attaches the UPC to your Guest ID in the CRM (customer relations
management) software, which then starts working on what you’ll want next.
This is just a sliver of the corporate data state, the full description of which
could take pages. For the government picture, a sliver is all I have, because
that’s all we’ve been able to see of it. We do know that the UK has 5.9 million
security cameras, one for every eleven citizens. In Manhattan, just below
Fourteenth Street, there are 4,176. Satellites and drones complete the picture
beyond the asphalt. Though there’s no telling what each one sees, it’s safe to
say: if the government is interested in your whereabouts, one sees you. And
besides, as Edward Snowden revealed, much of what they can’t put a lens on
they can monitor at leisure from the screen of an NSANet terminal, location
undisclosed.
Because so much happens with so little public notice, the lay understanding of
data is inevitably many steps behind the reality. I have to say, just pausing to
write this book, I’m sure I’ve lost ground. Analytics has in many ways surpassed
the information itself as the real lever to pry. Cookies in your web browser and
guys hacking for credit card numbers get most of the press and are certainly the
most acutely annoying of the data collectors. But they’ve also taken hold of a
small fraction of your life, and for that small piece they had to put in all kinds of
work. No matter how crafty the JavaScript, they’re villains in the silent-film
vein, all mustachios and top hats. Or, a more contemporary reference: they’re
like so many pasty Dr. Evils—underworld relics holding the world hostage for
one … million … dollars … while the billions fly by to the real masterminds, like
Acxiom. These corporate data marketers, with reach into bank and credit card
records, retail histories, and government filings like tax records, know stuff
about human behavior that no academic researcher, fishing for patterns on some
website, ever could.2 Meanwhile, the resources and expertise the national
security apparatus brings to bear makes enterprise-level data-mining software
look like Minesweeper.
This data, despite the “mining” metaphor, isn’t a naturally occurring resource;
it comes from somewhere—and that somewhere is you. The companies and the
government are collecting disparate pieces of your private life and trying to
fashion them back into an image they can master. The more privacy you lose, the
more effective they are. The fundamental question in any discussion of privacy
is the trade-off—what you get for losing it. We make calculated trades all the
time. Public figures sell their personal lives to advance their careers. Anyone
who’s booked a hostel in Europe or bought a train ticket in India has had to
decide if the private room is worth the extra money. And not to confuse the issue
here, but many people, men and women, trade on privacy when they walk out
the door in the evening, giving it away, via a hemline or a snug fit, for attention.
So the exchange isn’t new. But our trading partners, and their terms, are. On the
corporate side, the upshot of our data (the benefit to us) isn’t all that interesting
unless you’re an economist. In theory, your data means ads are better targeted,
which means less marketing spend is wasted, which means lower prices. At the
very least, the data they sell means you get to use genuinely useful services like
Facebook and Google without paying money for them. What we get in return for
the government’s intrusion is less straightforward.
Does surveillance make us more safe? Is the security apparatus a blanket?
Well, there haven’t been any terror attacks on American civilians since 2001—at
least, not ones by the syndicates. That’s not meaningless, certainly not to a New
Yorker. But an argument from absence isn’t very strong, and at least until we’re
allowed to know the threats that were thwarted as opposed to those never
planned, it’s hard to trust what we’re told. Like so much Texas dust, its memory
has almost drifted away, but the color-coded “Threat Level” that was such a part
of the discussion in the years after 9/11 always felt to me like an elaborate
advertisement for Halliburton. It’s hard to believe in information coming to you
on a “need to know basis” from an entity that doesn’t think you need to know
anything. The concern becomes less about what they’re saying than why. In any
event, I have no idea how many, if any, crimes the big glean at the NSA has
prevented. We’re told it works, just not when, where, or how.
Quixotically, for those crimes total surveillance didn’t prevent, it has certainly
proved useful in solving. All those security cameras cracked the case after the
Boston Marathon bombing, as they did after the London subway bombings in
2005.3 Especially for asynchronous crimes, you need total data to return to,
because the criminals commit their acts long before any victims fall. In these
investigations, the power of the intelligence becomes part of the media story—
this is the surveillance state’s time to shine. The data has a defined purpose, and
no one debates the privacy/protection balance while there is blood on the
ground. But in between the times of “United We Stand” a lot of what we learn
about what the government knows comes from whistle-blowers like Snowden.
The NSA is the government’s signals intelligence arm, and here the signal
they’re looking for is in our data. I have some personal familiarity with the
organization. As I’ve said, I studied math. I did so at Harvard. My bachelor’s
degree looks just like my classmates’, but there were unofficially two tracks in
the department. One, mine, was for the kids who liked math and were pretty
good at it. The other was for the transcendent savants. There was a difficult firstyear course called Math 25, which I wasn’t good enough for, and from which the
ultra-elite were drawn into a superclass called Math 55 by special invitation from
the department. The hardest courses I ever took were often entirely skipped by
these real mathematicians. The teaching assistants in my high-level courses, the
people who handled a lot of the actual instruction and all of the grading, were
not only often younger than me (one was sixteen) but were already deep into the
graduate-level curriculum, as teenagers. I remember being very excited about
(and challenged by) Real Analysis, which was a class that many of my peers—as
if that’s the right word—would’ve found boring as ninth-graders. Whenever I
hear the letters “NSA,” I think back to those days, because they recruited from
that second track.
I point this out because, to many people, government workers have an
indifferent reputation—bureaucrats, functionaries, whatever. And certainly the
average person working in data analytics in the private sector is as likely to be
competent as not. But the people spying on us are extremely, extremely smart.
We can hope that they, like Feynman and Einstein before them, are able to
temper their work with a farsighted humanity, but we can know, for sure, that,
like Feynman and Einstein before them, what they’re working on is inhumanly
powerful.
Insofar as algorithms are fed by data, Mr. Snowden has revealed that the
NSA’s are fatted on superfood. Or rather … all the food. They gather phone
calls, e-mail, text messages, pictures, basically everything that travels by electric
current. It’s clear that it’s not a passive operation—according to one leaked
document, the stated, top-level purpose is to “master the Internet.” The project’s
brazenness is one of the most phenomenal things about it. Among the first
documents published (jointly by the Guardian and the Washington Post) was a
PowerPoint presentation about a program called PRISM. The slides don’t beat
around the bush:
It should’ve been called Operation Yoink! On the one hand, life on Earth only
gets worse when anyone wearing a sidearm starts thinking about our Facebook
accounts. On the other, it’s hard to be afraid of people using the Draw tool in a
Microsoft product.
No one sees the PRISM data for an individual without a court order, at least in
theory, because the program is so invasive. Other snooping is mostly focused on
metadata—the incidentals of communication. Here’s the government’s own
Privacy and Civil Liberties Oversight Board describing one part of another
project:
For each of the millions of telephone numbers covered by the NSA’s
Section 215 program, the agency obtains a record of all incoming and
outgoing calls, the duration of those calls, and the precise time of day when
they occurred. When the agency targets a telephone number for analysis,
the same information [is obtained] for every telephone number with which
the original number has had contact, and every telephone number in contact
with any of those numbers.
It must be said that none of this entails the actual content of anyone’s
communication. In that respect, it’s not much different from the data we’ve
looked at in this book. We let patterns stand in for any single person’s life, just
like these guys do. At the NSA, again according to them, if your web of calls fits
the profile of a “threat,” only then do they start paying real attention. But
metadata isn’t necessarily less invasive for being indirect.
People leave some amazing breadcrumbs for anyone interested in following
them. You’ve seen plenty already—200 pages’ worth. Even so, there are just as
many trails we haven’t followed. For example, a little text file called the Exif is
attached to all images taken with a digital camera, from high-end SLRs to your
iPhone. The file encodes not only when the picture was taken but miscellany like
the f-stop and shutter speed for the photo and, often, the latitude and longitude of
where it was taken. Exif is how programs like iPhoto can effortlessly sort your
pictures into “moments” and place little pins all over the map to show you where
you’ve been. There are other things the Exif can tell you, though. Take the
profile photos on OkCupid. The better-looking a photo is, the better chance it
has of being outdated. That is, people find that one “great picture” and just lock
it in forever. We know this because of the Exif, which tells us when the picture
was taken. This kind of data tagalong is common. GPS coordinates ride shotgun
over the network whenever you open your favorite app. Almost every web page
you’ve ever loaded has dozens of one-pixel images (just a single transparent dot)
buried in the margins that, by being loaded alongside the “real” page, register
your visit; the pixels can’t tell what you’re doing, just when and where you’ve
gone. This simple stuff, just whens and wheres can give a company a good guess
at your whole demographic profile.
What about the people who don’t want to share like this? The people who
would rather shop and preen alone? I myself know the value of privacy. That’s
part of the reason I’m not a big social-media user, frankly. I have never posted a
picture of my daughter on the Internet. I started using Instagram in earlyish 2011
when the service wasn’t big yet, and I used it as just a photo gallery app because
I liked the filters. I thought it was like Hipstamatic, not really social—I know
this makes me sound like a grandfather. When my wife realized what her fuddyduddy husband was doing, she pointed out that I could connect my account to
other people’s accounts, which I did, because hey, look: a button to click. But
once it wasn’t just me on my own with my pictures, it lost all appeal.
This kind of reticence is unusual. For all the hand-wringing, it’s hard to argue
that most users are anything but blasé about privacy. Whenever Facebook
updates its Terms of Service to extend their reach deeper into our data, we rage
in circles for a day, then are on the site the next, like so many provoked bees
who, finding no one to sting, have nowhere to go but back to the hive. Because
tech loves to push boundaries and the boundaries keep giving, software has
gotten almost aggressively invasive. There are weight-loss apps. Heart-rate apps.
Rate-my-outfit apps—submit your ensemble to the crowd for fashion advice.
Women are using apps to predict and manage their menstrual cycle: “The market
is flooded with them,” as Jenna Wortham writes, before adding, “nearly every
woman I know uses one.” You let the app know when your period starts, and
it’ll alert you when you’re at peak fertility, to avoid or embrace as you wish. Of
course, self-reported data not being quite invasive enough, there’s a startup that
says it can infer when a woman is having her period from her link history. Any
of these menstruation apps—at least if they have a competent data scientist
behind them—will of course also know when a user is pregnant, overexercising,
getting older, or having unprotected sex, since when you’re late, you’ll check the
thing unusually often.
But despite some, even many, people’s cavalier attitude toward privacy, I
didn’t want to put anyone’s identity at risk in making this book. As I’ve said, all
the analysis was done anonymously and in aggregate, and I handled the raw
source material with care. There was no personally identifiable information (PII)
in any of my data. In the discussion of users’ words—their profile text, tweets,
status updates, and the like—those words were public. Where I had user-by-user
records, the userids were encrypted. And in any analysis the scope of the data
was limited to only the essential variables, so nothing could be tied back to any
individual.
I never wanted to connect the data back to individuals, of course. My goal was
to connect it back to everyone. That’s the value I see in the data and therefore in
the privacy lost in its existence: what we can learn. Jaron Lanier, author of Who
Owns the Future? and a computer scientist currently working at Microsoft
Research, wrote in Scientific American that “a stupendous amount of
information about our private lives is being stored, analyzed and acted on in
advance of a demonstrated valid use for it.” He’s unquestionably right about the
“tremendous amount,” but I take issue with his final clause. How does anything
ever become useful if it can’t be “acted on in advance of a demonstrated valid
use”? The whole idea of research science is predicated on exploration. Iron ore
was once just another rock until someone started to experiment with it. Mold on
bread spent millennia just making people sick until Alexander Fleming
discovered it also made penicillin.
Already data science is generating deep findings that don’t just describe, but
change, how people live. I’ve already mentioned Google Flu; launched in 2008,
it now tracks nascent epidemics in more than twenty-five countries. It’s not a
perfect tool, but it’s a start. Combined data is even being used to prevent disease,
not just minimize it. As the New York Times reported last year: “Using data
drawn from queries entered into Google, Microsoft and Yahoo search engines,
scientists at Microsoft, Stanford and Columbia University have for the first time
been able to detect evidence of unreported prescription drug side effects before
they were found by the Food and Drug Administration’s warning system.” The
researchers determined that paroxetine and pravastatin were causing
hyperglycemia in patients. Here, the payoff for living a little less privately is to
live a little more healthily.
Every day, it seems, brings word of some new advance. Today, I found out
that a site called geni.com is well on the way to creating a crowdsourced family
tree for all mankind. If it works, the company will have made, essentially, a
social network for our genetic material. The week before, two political scientists
debunked the received wisdom that Republicans owe their House majority to
district gerrymandering. The authors had modeled every possible election over
every possible configuration of the United States and concluded, with the
computer playing Candide, that our divided world is the best we can hope for.
The political geography of the country, not the actual maps, creates the gridlock.
This is just the beginning. Data has a long head start—Facebook was
collecting 500 terabytes of information every day way back in 2012—but the
analysis is starting to catch up. Data journalism was brought to the mainstream
by Nate Silver, but it’s become a staple of reporting: we quantify to understand.
The Times, the Washington Post, the Guardian have all built impressive analytic
and visualization teams and continue to devote resources to publishing the data
of our lives, even in the constrained financial climate for reporters and their
work.
On the flush corporate side, Google, mentioned many times in these pages,
leads the way in turning data to the public good. There’s Flu and the work of
Stephens-Davidowitz, but also a raft of even more ambitious, if less publicized,
projects, such as Constitute—a data-based approach to constitution design. The
citizens of most countries are usually only concerned with one constitution—
their own—but Google has assembled all nine hundred such documents drafted
since 1787. Combined and quantified, they give emerging nations—five new
constitutions are written every year—a better chance at a durable government
because they can see what’s worked and what hasn’t in the past. Here, data
unlocks a better future because, as Constitute’s website points out: in a
constitution, “even a single comma can make a huge difference.”
As we’ve seen, Facebook’s data team has begun to publish research of broad
value from their immense store of human action and reaction. Seizing on that
Newtonian interplay, Alex Pentland at MIT calls the emerging science “social
physics.” He and his team have begun moving social data to the physical world.
Working with local government, communications providers, and citizens,
they’ve datafied an entire city. The residents of Trento, Italy, can now tackle,
with hard numbers, what for the rest of us are workaday unanswerables: “How
do other families spend their money? How much do they get out and socialize?
Which preschools or doctors do people stay with for the longest time?”
Perhaps this is the future we have to look forward to. I’ve tried to explain
what we’ve already learned by combining the best of the work that’s out there
with my own original research. In so doing, more than stretching out my arms to
say This is the pinnacle, I mean to communicate the power of what’s to come.
Watson and Crick unlocked the secret of DNA in 1953, and six decades later
scientists are still decoding the human genome. The science of our shared
humanity—the search for the full expression of the genes we’ll soon have fully
mapped—is years from anything so lofty.
As far as balancing the potential good with the bad, I wish I could propose a
way forward. But to be honest I don’t see a simple solution. It might be that I’m
too close. I share Lanier’s belief that regulation won’t work. Not that someone
won’t try that route. The new laws will be drafted with all the right spirit, I’m
sure, but their letter will be outdated before the ink is dry. And being on the data
collectors’ side myself, I’ve seen firsthand that you can give people all the
privacy controls in the world, but most people won’t use them. OkCupid asks
women: Have you ever had an abortion?—it’s the 3,686th match question; I told
you they truly cover everything. Right beneath the question, there’s a checkbox
to keep your answer private. Of the people who answer in the affirmative, fewer
than half check the box.
So most people won’t use the tools you give them, but maybe “most people”
is the wrong goal here. For one thing, providing ways to delete, or even
repossess, data is the right thing to do, no matter how few users take you up on
it. For another, it’s possible that privacy has changed, and left the people writing
about it behind. Lanier and I are old men by Internet standards, and it’s not just
in armies that “generals always fight the last war.” My expectations of what is
correct and permissible might be wrong. Cultures and generations define privacy
differently.
People aren’t even that upset about the NSA, as gross as their overreach is.
There have been many “Million” marches on Washington. Million Man, Million
Mom, and so on. Recently, the hacker collective Anonymous called for a Million
Mask March to protest, among other things, the PRISM program and
government mass surveillance. The Washington Post captures the shortfall of
public interest in just the first word of their coverage: “Hundreds of
protesters …”
In his Scientific American piece, Lanier proposes that we be compensated for
our personal data and let market forces rebalance the privacy/value equation. He
proposes that data collectors issue micropayments to users whenever their data is
sold. But that expense, like a tax, either will be passed directly back to the
consumer or will bring on a race to the bottom, where websites have to find
margin wherever they can get it, the way commercial airlines do now. Either
way, there’s no net value in it for us. And that’s not to mention the impracticality
of making it happen.
Pentland’s approach is much more feasible: he calls it his “New Deal on
Data.” Ironically enough, it harkens back to Old English Common Law for its
principles. He believes that, as with any other thing you own, you should have
the fundamental rights of possession, use, and disposal for your data. What that
means is you should be able to remove your data from a website (or other
repository) whenever you feel like it’s being misused. You should also be
allowed to “take it with you,” in theory for resale, should a market for that
develop. That simple mechanism—the Delete button, with the option to
copy/paste—is not only more feasible but also more fair than any enforced
compensation.
In fact, on the corporate side, I would argue that people are already
compensated for their data: they get to use services like Facebook and Google—
connect with old friends, find what they’re looking for—for free. As I’ve said, I
give these services little of myself; but I get less out of them too. People have to
decide their own trade-off there. Soon, though, there might be only one decision
to make: am I going to use these services at all? The analytics are becoming so
powerful that it may not matter what you try to hold back. From only the barest
information, algorithms are already able to extrapolate or infer much about a
person; that’s after only a few years of data to work on. Soon the half measures
provided by menu options as you “manage your privacy settings” will give no
protection at all, because the rest of your world won’t be so withholding.
Companies and the government will find you through the graph. This whole
debate could soon be an anachronism.
In any event, when I talked about the data as a flood way, way back, I perhaps
didn’t emphasize it enough: the waters are still churning. Only when they start to
calm can people really know the level and make good the surfeit. I am eager to
do so. In the meantime, the people who store, analyze, and act on data have a
responsibility to continue to prove the value of their work—and to reveal exactly
what it is they’re doing. Or else, for all my quibbling, Lanier is right: we
shouldn’t be doing it.
Technology is our new mythos. There’s magic in some of it, undeniably. But
even grander than the substance is the image. Tech gods. Titans. Colossi astride
the whole Earth, because, you know, Rhodes just isn’t cool anymore. This is
how the industry is often cast to the public, and sadly it’s how it often thinks of
itself. But though there are surely monsters, there are no gods. We would all do
well to remember this. All are flawed, human, and mortal, and we all walk under
the same dark sky. We brought on the flood—will it drown us or lift us up? My
hope for myself, and for the others like me, is to make something good and real
and human out of the data. And while we do, whenever the technology and the
devices and the algorithms seem just too epic, we must all recall Tennyson’s
aging Ulysses and resolve to search for our truth in a slightly different way. To
strive, to seek, to find, but then, always, to yield.
1 From Nature’s discussion of the console: “It is fitted with a camera that can monitor the heart rate of
people sitting in the same room. The sensor is primarily designed for exercise games, allowing players to
monitor heart changes during physical activity, but, in principle, the same type of system could monitor and
pass on details of physiological responses to TV advertisements, horror movies or even … political
broadcasts.”
2 From Acxiom’s website: “[We give] our clients the power to successfully manage audiences, personalize
customer experiences and create profitable customer relationships.” An interesting paradox: whenever you
see the word “personalize,” you know things have gotten very impersonal.
3 After Boston, Reddit and 4chan tried vigorously (meaning there was lots of typing) to track down the
bombers and eventually “pinned” it on an innocent man. For all the lip service the cloud and crowd get,
hardware solved the crime.
Coda
Designing the charts and tables in this book, I relied on the work of the
statistician and artist Edward R. Tufte. More than relied on, I tried to copy it. His
books occupy that smallest of intersections: coffee-table beautiful and textbook
clear, and inside he lays out principles of information design drawn from the alltime famous examples of data as storytelling. Charles Minard’s plot of
Napoleon’s Russian undoing. An unnamed abolitionist’s Description of a Slave
Ship, showing the human cargo packed in inhuman closeness, an image that is
still the iconic shorthand for the horrors of the Middle Passage. Dr. John Snow’s
plot of a cholera outbreak in 1854 pinpointed the source of the disease for the
first time. Tufte pulls lessons from these and makes them useful in a modern
context, asking the data designer to maximize the data-to-ink ratio. Give every
chart a clear story to tell. Use color to call out data’s red heart. Use white as
dimension, not dead space. I’ve tried my best.
Among the many maps and charts and tables in Tufte’s books, there’s a twopage examination of the Vietnam Memorial, not as stonework or as history, but
as an artifact of data design. I wish I could reprint the full discussion here, but
the kernel is this:
From a distance the entire collection of names of 58,000 dead soldiers
arrayed on the black granite yields a visual measure of what 58,000 means,
as the letters of each name blur into a gray shape, cumulating to the final
toll.
To find meaning in that gray blur is what every data scientist hopes for, and
I’ve sought that distance and that blur repeatedly in these pages, drawing from
the biggest data sets, looking at the widest stories, all to better my chances at
truth.
The memorial was digitized in 2008. Every square inch was photographed and
collated with military records, and the online version allows visitors to attach
photos and text to each name. The web archive confronts the visitor with an
empty box, demanding, “Search the Wall.” After a pause, I started to type my
dad’s name, because when I think of Vietnam I think of him almost as a reflex.
But then I remembered, gratefully, David Patton Rudder isn’t on this list. So I
entered someone’s name, just a guess—“John” of course and then because Smith
seemed too bland and Doe too hokey, “Wilson.” The page churned for a half
second, and at the top I saw:
Lorne John Wilson
Tour Start Date 1969-03-17
Tour End Date 1969-03-28
Death Date 1969-03-28
Age 20
Two pictures had been added to his entry, one his portrait in dress blues, the
other a snapshot, perhaps taken one of those eleven days PFC Wilson was incountry and alive. It shows four young men around a jeep, one’s standing in the
back; they’re just talking in the afternoon. Grainy and undersaturated, but for the
fatigues it could’ve come from Instagram. Whoever uploaded it had held on to
the picture, and his friends, for decades.
A web page can’t replace granite. It can’t replace friendship or love or family,
either. But what it can do—as a conduit for our shared experience—is help us
understand ourselves and our lives. The era of data is here; we are now recorded.
That, like all change, is frightening, but between the gunmetal gray of the
government and the hot pink of product offers we just can’t refuse, there is an
open and ungarish way. To use data to know yet not manipulate, to explore but
not to pry, to protect but not to smother, to see yet never expose, and, above all,
to repay that priceless gift we bequeath to the world when we share our lives so
that other lives might be better—and to fulfill for everyone that oldest of human
hopes, from Gilgamesh to Ramses to today: that our names be remembered, not
only in stone but as part of memory itself.
A Note on the Data
Numbers are tricky. Even without context, they give the appearance of fact, and
their specificity forbids argument: 20,679 Physicians say “LUCKIES are less
irritating.” What else is there to know about smoking, right? The illusion is even
stronger when the numbers are dressed up as statistics. I won’t rehash the old
wisdom there. But behind every number there’s a person making decisions: what
to analyze, what to exclude, what frame to set around whatever pictures the
numbers paint. To make a statement, even to just make a simple graph, is to
make choices, and in those choices human imperfection inevitably comes
through. As far as I know, I’ve made no motivated decision that has bent the
outcome of my work—the data of people acting out their lives is interesting
enough without me needing to lead it one way or another. But I have made
choices, and those choices have affected the book. I’d like to walk you through a
few of them.
My first choice was probably my most difficult: the decision to focus on malefemale relationships when I talk about attraction and sex. Space, of course, was a
factor—to include same-sex relationships would’ve meant repeating each graph
or table in triplicate. But more than that was the discovery that same-sex
relationships aren’t exceptional—they follow all the same trends. Gay men, for
example, prefer younger partners just like straight men do. For issues that have
to do with sex only indirectly, such as ratings from one race to another, gays and
straights also show similar patterns. Male-female relationships allowed for the
least repetition and widest resonance per unit of space, so I made the choice to
focus on them.
My second decision, to leave out statistical esoterica, was made with much
less regret. I don’t mention confidence intervals, sample sizes, p values, and
similar devices in Dataclysm because the book is above all a popularization of
data and data science. Mathematical wonkiness wasn’t what I wanted to get
across. But like the spars and crossbeams of a house, the rigor is no less present
for being unseen. Many of the findings in the book are drawn from academic,
peer-reviewed sources. I applied the same standards to the research I did myself,
including a version of peer-review: much of the OkCupid analysis was
performed first by me and then verified independently by an employee of the
company. Also, I separated the analysis from the selection and organization of
the data to make sure the former didn’t motivate the latter. One person would
extract the information, another would try to figure out what it meant.
Sometimes, I present a trend and attribute a cause to it. Often that cause is my
best guess, given my understanding of all the forces in play. To interpret results
—a necessity in any book that isn’t just reams of numbers—I had to choose one
explanation from a variety of possibilities. Is there some force besides age
behind what I call Wooderson’s law (the fact that straight men of all ages are
most interested in twenty-year-old women)? Perhaps. But I think it is very
unlikely. “Correlation does not imply causation” is a good thing for everyone to
keep in mind—and an excellent check on narrative overreach. But a snappy
phrase doesn’t mean that the question of causation isn’t itself interesting, and
I’ve tried to attribute causes only where they are most justified.
For almost all the parts of Dataclysm that overlap with posts on OkCupid’s
blog, I chose to redo the work from scratch, on the most recent data, rather than
quote my own previous findings. I did so because, frankly, I wanted to doublecheck what I’d done. The research published there from 2009 through 2011 was
put together piecemeal. Many different people—I can count at least five—had
pulled male-female message-reply rates for me over those three years, just to
name one frequently used data point, and going back through my records of this
data, there was no way to be sure what data set had generated the results. Doing
it again myself, I could be sure. I could also enforce a uniform standard across
all my research (for example, restricting analysis to only people ages twenty to
fifty—a choice I made because those are the ages where I knew I had
representative data).
Because the research is new, the numbers printed in Dataclysm are different
from the numbers on the blog. Curves bend in slightly new ways. Graphs are a
bit thicker or perhaps a bit thinner in places. The findings in the book and on the
blog are nonetheless consistent. Ironically, with research like this, precision is
often less appropriate than a generalization. That’s why I often round findings to
the nearest 5 or 10 and the words “roughly” and “approximately” and “about”
appear frequently in these pages. When you see in some article that “89.6
percent” of people do x, the real finding is that “many” or “nearly all” or
“roughly 90 percent” of them do it, it’s just that the writer probably thought the
decimals sounded cooler and more authoritative. The next time a scientist runs
the numbers, perhaps the outcome will be 85.2 percent. The next time, maybe
it’s 93.4. Look out at the churning ocean and ask yourself exactly which
whitecap is “sea level.” It’s a pointless exercise at best. At worst, it’s a
misleading one.
If you trace the findings in Dataclysm back to the original sources, the
OkCupid data isn’t the only place you’ll see discrepancies. This data of our
lives, being itself practically a living thing, is always changing. For example, my
Klout score, which is holding steady at 34 as I write these words, will have no
doubt gone up by the time you read them, since part of my obligation to Crown
will be to tweet about this book. User engagement, ho!
Sometimes the numbers shift for no obvious reason. My copy editor and I had
a mess of a time pinning down the Google autocompletes for prompts like “Why
do women …” Google had given each of us slightly different results (“… wear
thongs?” was my third result to the above, presumably because that’s a typically
male question [?]. Hers was “… wear bras?”). Then when I checked a few weeks
later, I myself saw something different: “… wear high heels?” Since it was the
most recent result, that’s what ended up in the book.
As interesting a tool as it is, the black box of Google’s autocomplete (and
Google Trends, for that matter) is an example of one of the worst things about
today’s data science—its opaqueness. Corroboration, so important to the
scientific method, is difficult, because so much information is proprietary (and
here OkCupid is as guilty as anyone). Even as most social media companies
trumpet the hugeness and potential of their data, the bulk of it has stayed offlimits to the larger world. Data sets currently move through the research
community like yeti—I have a bunch of interesting stuff but I can’t say from
where; I heard someone at Temple has tons of Amazon reviews; I think L has a
scrape of Facebook. That last is something I was told by three unrelated
academics; they referred to another scientist by name, which I’ve here obscured.
L does in fact have that rogue Facebook scrape—I met him and confirmed—but
he can’t show it to anyone. He’s really not supposed to have it at all. Data is
money, which means companies treat it as such—and though some digital data
sits out in the open, it’s secured behind legal walls as thick as any vault’s. If you
look at your friend Lisa’s Facebook page, observe that her name is Lisa, and
publish that fact (anywhere!)—you have technically stolen Facebook’s data. If
you’ve ever signed up for a website and given a fake zip code or a fake birthday,
you have violated the Computer Fraud and Abuse Act. Any child under thirteen
who visits newyorktimes.com violates their Terms of Service and is a criminal—
not just in theory, but according to the working doctrine of the Department of
Justice.1 The examples I’ve laid out are extreme, sure, but the laws involved are
so broadly written as to ensure that, essentially, every Internet-using American is
a tort-feasing felon on a lifelong spree of depraved web browsing. Whether
anyone penalizes you for your “crime” is another matter, but, legally, you are
prostrate, a boot on your neck. A company’s general counsel, or a district
attorney looking to please an important corporate donor, can destroy your life
simply by deciding to press. When it suits, they do. So social scientists are very
cagey with data sets; actually, more than yeti, they treat them like big bags of
weed—possessive, slightly paranoid, always curious who else is holding and
how dank that shit is.
Increasingly the preferred practice is to bring researchers in-house rather than
release information outside.2 And that approach has yielded, among many fruits,
the novel research by Facebook’s data team and Seth Stephens-Davidowitz’s
fine work at Google, both of which I’ve drawn on here. I hope more companies
follow this model, and that eventually we, the owners of the sites, will find a
way to release our data for the public good without jeopardizing our users’
privacy in the act.
It’s old hat now, but the app Shazam was, to me, one of the first great wonders
of the iPhone. It’s a little program for identifying music—if some song is
playing, and you want to know what it is, you just turn on the app and hold up
your phone. Shazam listens through the microphone, and, like, two seconds later,
it tells you what you’re listening to. The first time someone did it in front of me,
I was just blown away, not only at how little the software needed to get the song
right (it can often work through walls or above the din of a bar), but at how fast
it worked. It was the closest thing I’d seen to magic, at least until I came to know
a certain able necromancer who, at a whim, could summon fees and add them to
my goddamn kitchen renovation. But anyway, as I later found out, Shazam relies
on an incredible principle: that almost any piece of music can be identified by
the up/down pattern in the melody—you can ignore everything else: key,
rhythm, lyrics, arrangement … To know the song, you just need a map of the
notes’ rise and fall. This melodic contour is called the song’s Parsons code,
named after the musicologist who developed it in the 1970s. The code for the
first two lines of “Happy Birthday” is •RUDUDDRUDUD, with U meaning
“melody up,” D meaning “melody down,” and R for “repeated note.” The dot •
just marks the beginning of the tune, which of course isn’t up or down from
anything. Hum it to yourself to check:
As crazy as it seems, the code for “Happy Birthday” is practically unique across
the entire catalog of recorded music, as is the code for almost all songs. And it’s
because these few letters are such a concise description that Shazam is so fast:
instead of a guitar, Paul McCartney, and just the right amount of reverb,
“Yesterday” starts with •DRUUUUUUDDR. That’s a lot easier to understand.
Like an app straining for a song, data science is about finding patterns. Time
after time, I—and the many other people doing work like me—have had to
devise methods, structures, even shortcuts to find the signal amidst the noise.
We’re all looking for our own Parsons code. Something so simple and yet so
powerful is a once-in-a-lifetime discovery, but luckily there are a lot of lifetimes
out there. And for any problem that data science might face, this book has been
my way to say: I like our odds.
1 For more on the Kafkaesque implications of the CFAA, please see “Until Today, If You Were 17, It
Could Have Been Illegal to Read Seventeeen.com Under the CFAA” and “Are You a Teenager Who Reads
News Online? According to the Justice Department, You May Be a Criminal,” both published by the
Electronic Frontier Foundation.
2 I wish this were called hotboxing, but sadly, no.
Notes
We no longer live in a world where a reader depends on endnotes for “more
information” or to seek proof of facts or claims. For example, I imagine any
reader interested in Sullivan Ballou will have Googled him long before she
consults these notes and transcribes into her browser the links I’ve provided. So I
have used this section to focus on the many sources that have contributed not
only facts but ideas to this book. I’ve also used it to substantiate or explain
claims about my own proprietary data.
Since the subject of Dataclysm is changing almost daily, I’ve decided to
enhance this section online at dataclysm.org/endnotes, where you will find
additional source material and findings from emerging research.
Introduction
10 million people will use the site For this number, I counted every person who
logged into OkCupid in the twelve months trailing April 2014: 10,922,722.
Tonight, some thirty thousand couples It’s the great unknowable of running
an online dating site: How many of the users actually meet in person? And
what happens next? This passage represents my best guesses at some basic
in-person metrics. I used two separate methods:
1. I assumed someone who’s actively using OkCupid goes on one date
every other month. I think this is conservative. At roughly 4,000,000 active
users each month, that means roughly 65,000 people go on dates each day,
meaning roughly 30,000 couples.
2. Every day 300 couples wind their way through our “account disable”
interface to let us know that they no longer need OkCupid specifically
because they have found a steady relationship on OkCupid. These are
couples who (a) are dating seriously enough to shut down their OkCupid
accounts, and who (b) are willing to go through the trouble of filling out a
bunch of forms to let us know their new relationship status. I estimate that
Group B represents only 1 in 10 of the long-term couples actually created by
the site. And I estimate that Group A represents the outcome of only 1 in 10
first dates. Therefore, there must be 3,000 long-term couples, from 30,000
first dates each day. Of every 3,000 long-term couples, I believe something
less than 1 in 10 go on to get married. One way to look at this: How many
serious relationships did you have before you found the person you settled
down with? I imagine the average number is roughly 10.
These appraisals together are mutually supporting, at least of the “first
dates” number, and even if it’s approximate, I think the deeper metrics
follow plausibly.
ratings of pizza joints on Foursquare Ratings from a random sample of 305
New York City pizza places accessed through Foursquare’s public API.
the recent approval ratings for Congress These were collected from the 529
polls measuring “congressional job approvals” listed on the site real
clearpolitics.com from January 26, 2009, through September 14, 2013. See
realclearpolitics.com/epolls/other/congressional_job_approval903.html#polls.
NBA players by how often The chart shows percent of games started for each
of the players listed on a team roster for the 2012–2013 season on espn.com.
Yes, I’m counting the 76ers as an NBA team.
6 percent This number comes from taking the geometric mean of the distances
between each of the 21 discrete data points along the curves. So, for curves a
and b, I calculated:
Which equals 0.056.
58 percent of men The male attractiveness curve is centered more than a
whole standard deviation below the female. Translating the same disparity to
IQ means that the median male IQ would be slightly lower than 85, which is
the threshold for “borderline intellectual functioning.” For example, the US
Army doesn’t accept applicants with IQs below 85. I say “brain damaged” as
a bit of hyperbole meant to capture this shift. Strictly speaking, I mean that
58 percent of men would have IQs lower than 85.
half the single people in the United States Specifying the reach of the dating
data I have was a challenge. I’ve strived to do so in broad, easy-to-grasp
terms because, unlike Facebook or Twitter, I know much of my reading
audience has never used a dating site. If you’ve been married or in a
relationship since the late ’90s or before, you have never needed online
dating. According to the 2011 Census numbers, there are 103 million single
people ages fifteen to sixty-four in the United States—that counts everyone
who isn’t legally married, including many people who are actually in longterm relationships and nearly every gay person. Together, Tinder, OkCupid,
DateHookup, and Match.com registered 57 million US accounts from 2011
to 2013, and 23 million in the last of those three years alone. “Half” is my
approximation of 57/103, minus the 10 to 15 percent wastage in overlap and
duplicate accounts.
“Women are inclined to regret” This quote is from the “Findings” section of
the February 2014 issue of Harper’s by Rafil Kroll-Zaidi.
A beta curve plots My data researcher, Tom Quisel, helped me put the binomial
nature of beta curves into simple terms. He also pointed out that they’re used
to model weather, and ran the comparisons to the by-city patterns on
weatherbug.com.
Some 87 percent of the United States is online See Susannah Fox and Lee
Rainie, “Summary of Findings,” Pew Research Internet Project, Pew
Research Center, February 27, 2014, pewinternet.org/2014/02/27/summaryof-findings-3/.
that number holds … For example, Internet use among white, African
American, and Hispanic Americans is 85, 81, and 83 percent, respectively.
One can only assume adoption among Asian Americans is similar. Adoption
is above 80 percent for all age groups, save people sixty-five and older.
Susannah Fox and Lee Rainie, “Internet Users in 2014,” Pew Research
Internet Project, Pew Research Center, February 27, 2014,
pewinternet.org/files/2014/02/12-internet-users-in-2014.jpg.
More than 1 out of every 3 Americans access Facebook Facebook reported 128
million US users in August 2013. Facebook had at least 1.26 billion users
worldwide in September 2013. World and US population statistics are from
Wikipedia. See expandedramblings.com/index.php/by-the-numbers-17-
amazing-facebook-stats/.
fundamentally populist This is something like common knowledge among
people who study social media adoption beyond the Google
Glasshole/Technocrat use case. See Pew Research Center’s “Demographics
of Key Social Networking Platforms” (2013). The report shows no
statistically significant difference in rates of Twitter use between the “high
school grad or less” and “College +” educational cohorts (coming in at 17
percent and 18 percent, respectively). Pew surveys a random cross-section of
Americans eighteen years old or older, so very few of the “high school grad
or less” cohort are that way simply because they’re still in high school. By
ethnicity, Pew reports adoption rates of 29 percent among blacks and 16
percent among both whites and Hispanics. The full report, by Maeve Duggan
and Aaron Smith, is here: pewinternet.org/2013/12/30/demographics-of-keysocial-networking-platforms/.
It’s called WEIRD research This fact and my general take on the phenomenon
are adapted from “Psychology Is WEIRD,” by Bethany Brookshire, in Slate.
See also “The Roar of the Crowd,” The Economist, May 24, 2012,
economist.com/node/21555876.
Pharaoh Narmer As you can imagine, this is up for debate, though Narmer, also
known as Serket, is a defensible choice. In earlier drafts I had Gilgamesh, the
Akkadian hero, in this place because J. M. Roberts, in his History of the
World (New York: Oxford University Press, 1993), chooses Gilgamesh. I
eventually went with Narmer because his life is dated several centuries
earlier, and he seemed to me as likely to have actually lived. Yahoo!
Answers also mentions Elvis Presley.
Chapter 1: Wooderson’s Law
This isn’t survey data This is a good place to point out that for anyone’s
attractiveness to have been considered in my analysis in this book, that
person needed to have received votes from at least twenty-five other people.
For something as idiosyncratic as attraction, I felt an average score
comprising fewer than twenty-five votes wasn’t reliable.
per the US Census These numbers are from the US Census Bureau’s “Marital
Status of People 15 Years and Over, by Age, Sex, Personal Earnings, Race,
and Hispanic Origin, 2011.”
Chapter 2: Death by a Thousand Mehs
“Beauty is looks you can never forget” John Waters, Shock Value: A Tasteful
Book About Bad Taste (Philadelphia: Running Press, 2005), p. 128.
concept called variance I used standard deviation to measure variance
throughout this chapter.
the “pratfall effect” A Google search for “pratfall effect” will yield many
examples. I particularly relied on the précis “The Positive Effect of Negative
Information” by Bill Snyder and the original paper he summarizes, “When
Blemishing Leads to Blossoming: The Positive Effect of Negative
Information,” by Danit Ein-Gar, Zakary Tormala, and Shiv Tormala, Journal
of Consumer Research 38, no. 5 (2012): 846–59.
Our sense of smell For this passage, I relied on Fabian Grabenhorst et al.,
“How Pleasant and Unpleasant Stimuli Combine in Different Brain Regions:
Odor Mixtures,” Journal of Neuroscience 27, no. 49 (2007): 13532–40, doi:
10.1523/JNEUROSCI.3337–07.2007. Wikipedia’s “Indole” entry describes
its “intense fecal smell.” For more on indole’s role in perfumes and in
naturally occurring flower scents, see, as I did,
perfumeshrine.blogspot.com/2010/05/jasmine-indolic-vs-non-indolic.html.
Here are six women We received these permissions using a double-blind
system, to protect user privacy. I submitted criteria (women, high variance
scores, midrange overall attractiveness) to OkCupid’s data team. The data
team generated a list of possible names, which they passed on to our admin.
She then had a list of names, with no other information attached, and was
told to contact them for blanket photo authorization. (We commonly receive
press requests for user photos, so this type of outreach isn’t unusual.) A
photo and its unique attributes were only connected once permission was
granted.
Chapter 3: Writing on the Wall
Nostalgia used to be called Because the phenomenon is so interesting (and
unexpected) and one link leads to another, my sources for this passage were
many. These I drew on directly:
“Dying to Go Home,” by Jackie Rosenhek, Doctor’s Review, December
2008, doctorsreview.com/history/dying-to-go-home/.
“Beware Social Nostalgia,” by Stephanie Coontz, New York Times, May 19,
2013, nytimes.com/2013/05/19/opinion/sunday/coontz-beware-socialnostalgia.html.
“When Nostalgia Was a Disease,” by Julie Beck, The Atlantic, August 2013,
theatlantic.com/health/archive/2013/08/when-nostalgia-was-adisease/278648/.
The “Nostalgia” entry on qi.com: qi.com/infocloud/nostalgia.
people under eighteen aren’t using Facebook The earnings call in question
reviewed Facebook’s fourth-quarter performance, 2013. See Joanna Stern,
“Teens Are Leaving Facebook and This Is Where They Are Going,”
ABCNews, October 31, 2013, abcnews.go.com/story?id=20739310.
Major Sullivan Ballou The basic facts surrounding the letter can be found
here: pbs.org/civilwar/war/ballou_letter.html. Though the letter was never
mailed, it was included with Ballou’s belongings and returned to his family
after his death.
There will be more words written on Twitter I calculate this as follows:
129,864,880 books have been written, at least according to Google. That
number is laughably precise; however, given that they have already logged
30 million of them, and indexing things is their business, their guess should
be considered a plausible estimate. See Ben Parr, “Google: There Are
129,864,880 Books in the Entire World,” Mashable, August 5, 2010,
mashable.com/2010/08/05/number-of-books-in-the-world/.
According to Amazon, the median length of a novel is 64,000 words.
Since it’s very likely that the median and mean are close here, I’m
comfortable using it as an average. I don’t think novels are necessarily
longer or shorter than other books. See Gabe Habash, “The Average Book
Has 64,500 Words,” PWxyz, March 6, 2012,
blogs.publishersweekly.com/blogs/PWxyz/2012/03/06/the-average-bookhas-64500-words.
These two numbers together yield 8,311,352,320,000 words ever in print.
Twitter reported 500 million tweets a day in August 2013. See
blog.twitter.com/2013/new-tweets-per-second-record-and-how.
I estimate that each tweet has 20 words. So at 10 billion words a day, it
will take Twitter 831 days (2.3 years) to surpass all of printed literature in
volume. This is obviously meant to be an approximation, and a conservative
one at that. In all likelihood, Twitter will do it much faster, since the rate of
tweets per day is increasing rapidly.
“You only have to look on Twitter” Mr. Fiennes’s quote was covered
extensively. See Lucy Jones, “Ralph Fiennes Blames Twitter for ‘Eroding’
Language,” Telegraph, October 27, 2012,
telegraph.co.uk/technology/twitter/8853427/Ralph-Fiennes-blames-Twitterfor-eroding-language.html.
Even basic analysis shows Here and in all my own Twitter analysis I use the
tweets and followers generated by a representative corpus of 1.2 million
accounts, collected at random by my research team.
The OEC is the canonical census More on the OEC and its most common
words can be found here:
en.wikipedia.org/wiki/Most_common_words_in_English.
The OEC lists only lemmas—that is, the base word root of a related lexical
pattern. For example, it counts have for had, having, has, and so on. I chose
not to do this in my Twitter research. Though my choice makes comparing
the lists directly more difficult, I preferred to present the data in as raw a
state as possible.
Mark Liberman Professor Liberman’s blog Language Log
(languagelog.ldc.upenn.edu/nll/) contains a trove of interesting textual
analysis. See “Up in UR Internets, Shortening All the Words,” October 28,
2011, languagelog.ldc.upenn.edu/nll/?p=3532, for his discussion of the
Fiennes quote in particular.
A team at Arizona State The Twitter textual analysis in the rest of this
paragraph is drawn from “Dude, srsly?: The Surprisingly Formal Nature of
Twitter’s Language,” by Yuheng Hu, Kartik Talamadupula, and Subbarao
Kambhampati, paper presented at the seventh annual International AAAI
Conference on Weblogs and Social Media, Cambridge, Massachusetts, July
8–11, 2013, aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6139.
Here I’ve excerpted an early attempt The table and the subsequent discussion of
the word “tribes” on Twitter are drawn from “Word Usage Mirrors
Community Structure in the Online Social Network Twitter,” by John
Bryden, Sebastian Funk, and Vincent AA Jansen, EPJ Data Science 2, no. 3
(2013). I also draw from their “Additional Material” containing raw
community word lists not used in the paper itself. The full paper, along with
links to the additional material, can be found here:
epjdatascience.com/content/2/1/3.
This body of data has created a new field This method of mining Google Books
for cultural trends was first proposed in Science in the article “Quantitative
Analysis of Culture Using Millions of Digitized Books,” by Jean-Baptiste
Michel et al., Science 331, no. 6014 (2011): 176–82,
doi:10.1126/science.1199644.
My graph of food words over time is a reproduction of their exploration of
the same terms in that paper. My graph of year words over time is an
adaptation of their method, rather than a reproduction. The paper references
a “half-life” of memory that I was not able to reproduce. Nonetheless, the
writers’ claim that “We are forgetting our past faster with each passing year”
is clearly directionally correct. The paper has much more of interest than just
the two charts I’ve referenced here and is worth reading in full.
Below is a scatter chart of 100,000 messages No private messages were read by
anyone in performing this analysis. The number of keystrokes and typing
time are logged automatically for a sample of OkCupid’s users as part of our
ongoing spam-detection software. Since I didn’t read any actual user
messages, the quoted text of the three-letter message “hey” is a likelihood
rather than a certainty. About 80 percent of three-letter messages on the site
are “hey.” “Sup” is the next most popular, then “wow.” Given the
overwhelming popularity of “hey,” and that I was making a joke, and that
any of the alternatives would’ve worked just as well, I was comfortable
picking “hey” in this context.
“I’m a smoker too” This private message, presented verbatim and complete,
came to my attention in a context outside this book, and I received the
sender’s permission to both reprint and discuss it here.
Chapter 4: You Gotta Be the Glue
“social graphs” The network plots on this page and this page were generated
by James Dowdell, using the same general graphic scheme used by Lars
Backstrom and Jon Kleinberg in their paper “Romantic Partnerships and the
Dispersion of Social Ties: A Network Analysis of Relationship Status on
Facebook,” presented at the 18th ACM Conference on Computer-Supported
Cooperative Work and Social Computing, Baltimore, Maryland, February
15–19, 2014, delivery.acm.org/10.1145/2540000/2531642/p831-
backstrom.pdf.
I spent years touring in a band My band is called Bishop Allen; Justin Rice is
the band’s other half. You can find our songs on Spotify, or on the nearest
torrent, or on iTunes. For anyone interested, my personal recommendations
are the songs “Like Castanets,” “Click Click Click Click,” “Chinatown Bus,”
“Start Again,” and “Little Black Ache.”
In 1735, Leonhard Euler Though I was familiar with Euler, the bridges
problem, and their role in the genesis of graph theory from my time as a
math major, I relied on Wikipedia’s “Seven Bridges of Königsberg” entry for
the minutiae surrounding the problem and its solution.
has since helped us understand A good resource for both classic and modern
uses of graph theory is here: world.mathigon.org/Graph_Theory.
Stanley Milgram Like Euler, Milgram and his work have been familiar to me
for years. However, I relied on his Wikipedia entry for the details of his “Six
Degrees” experiment.
Facebook allowed us to see See “The Anatomy of the Facebook Social
Graph,” by Johan Ugander et al. (arXiv preprint, 2011, arXiv: 1111.4503).
Pixar famously put The idea was Steve Jobs’s. I first heard of this anecdote in
Jonah Lehrer’s Imagine (Edinburgh, UK: Canongate, 2012). See BuzzFeed’s
“Inside Steve Jobs’ Mind-Blowing Pixar Campus,” by Adam B. Vary, for
more details. Vary mind-blowingly interviews Craig Payne, a senior Pixar
manager: buzzfeed.com/adambvary/inside-steve-jobs-mindblowing-pixarcampus.
“the strength of weak ties” See “The Strength of Weak Ties” by Mark S.
Granovetter, American Journal of Sociology 78, no. 6 (1973): 1360–80.
Another long-held idea in network theory Though embeddedness was first
proposed by Granovetter in 1985, my remaining discussion of embeddedness
and of interpersonal network theory is drawn from the primary source behind
this chapter, Backstrom and Kleinberg’s “Romantic Partnerships.” I apply
their heuristic to my own networks and somewhat simplify their original
work for a nonacademic audience.
an astounding 75 percent of the time Backstrom and Kleinberg define many
subtly different mathematical kinds of dispersion. My number here refers to
the accuracy they reported with the method they call “recursive dispersion.”
50 percent more likely This is drawn from the following passage in Backstrom
and Kleinberg’s paper: “We find that relationships on which recursive
dispersion fails to correctly identify the partner are significantly more likely
to transition to ‘single’ status [that is, break up] over a 60-day period. This
effect holds across all relationship ages and is particularly pronounced for
relationships up to 12 months in age; here the transition probability is
roughly 50% greater when recursive dispersion fails to recognize the
partner.”
Have a meeting with Microsoft people This might not be broadly true of all
Microsoft employees; however, the teams responsible for Microsoft’s mobile
and tablet products are, in my experience, dogfooders of the first order.
Windows mobile is so rare as to be especially noteworthy, so you remember
it when you see it. This is a good place to point out that I am a lifelong user
of Microsoft Office, and all the charts and much of the analysis in this book
were done in Excel.
Chapter 5: There’s No Success Like Failure
one of Google’s best designers Douglas Bowman leaving Google is a famous
event in tech circles. See his own post “Goodbye, Google” at
stopdesign.com/archive/2009/03/20/goodbye-google.html.
no evidence of people gaming the system It was fairly simple to unscramble a
Crazy Blind Date photo; we knew this would be the case. Sure enough, about
a week after launch a few hackers had built apps to de-anonymize the photos.
However, these apps never caught on, mostly because they were difficult to
use and even then only worked part of the time. These unscramblers were not
a factor in Crazy Blind Date’s product trajectory or the data it generated. The
scrambled example photo printed in the book is a stock photo, licensed from
Getty Images.
Chapter 6: The Confounding Factor
of a certain type See, for example, “Blacks Still Dying More from Cancer
Than Whites,” by Jordan Lite, Scientific American, February 2009. Also see
the Sentencing Project’s “Criminal Justice Primer for the 111th Congress,”
which details many depressing disparities in the sentences handed down to
whites, compared to minority defendants:
sentencingproject.org/doc/publications/cjprimer2009.pdf.
conclusions like this The headline cited is from ThinkProgress.org. “Study:
Black Defendants Are at Least 30% More Likely to Be Imprisoned Than
White Defendants for the Same Crime,” by Inimai Chettiar, August 30,
2012, thinkprogress.org/justice/2012/08/30/770501/study-black-defendantsare-at-least-30-more-likely-to-be-imprisoned-than-white-defendants-for-thesame-crime.
in the 97,000 results It’s a bit of a hack to get Google to give you a number
here. My exact query was for “ ‘black quarterback’ −adsffsdada.” Using the
minus sign with the nonsense word keeps the page from automatically
returning images instead of the “about 97,000 results” text. I’m sure without
the browser in front of you, this all sounds mystifying. Try it yourself if you
care, and you’ll see immediately what I mean. Also, this is another example
of a raw number that has changed during the course of writing this book. I’ve
also gotten “89,800 results” returned to me.
I found only one article See Jason Lisk, “Quarterbacks and Whether Race
Matters,” The Big Lead, December 2, 2010,
thebiglead.com/2010/12/02/quarterbacks-and-whether-race-matters/. Of
course, the fact that I found only one writer who calculates quarterback
rating by race is hardly proof that no other writer has made the calculation.
However, I spent several hours combing results and found only Lisk.
the four largest racial groups 15 percent of OkCupid users who select an
ethnicity select more than one race; 3 percent select a race other than the four
largest. These people are excluded from the analysis, as are people who
neglected to choose a race at all.
“normalize” each row I normalized against the simple average in each row,
rather than the weighted average. Because of the preponderance of white
people, the latter technique would’ve skewed the matrix, functionally using
what everyone thinks of white people as the “norm.” A simple average
captures the following: “When a person of race A meets an arbitrary person
of race B, how does A appraise B, relative to A’s appraisals of other races?”
That’s the interesting question, and what we want to investigate.
There is no cadre of racists An analysis of individual bias applied by nonblack men to black female profiles shows a median deduction of 0.6 stars,
with most of the sample applying a deduction from 0.2 to 1.0 stars. 82
percent of the sample shows at least some consistent anti-black bias.
Here are our numbers Though the numbers I list for OkCupid here were
generated from internal data, you can see those numbers corroborated and
compared to Quantcast’s national averages by visiting
https://www.quantcast.com/okcupid.com?country=US. Select “Ethnicity”
from the Demo-graphics menu and expand the “US average” feature.
OkCupid users putting it in their own words These excerpts are from usersubmitted “Success Stories” published on the site. Bella and Patrick’s is
here: https://www.okcupid.com/success/story?id=2855. Dan and Jenn’s is
here: https://www.okcupid.com/success/story?id=2587.
“There are very few” Barack Obama’s quote is excerpted from his comments on
the George Zimmerman verdict: whitehouse.gov/the-pressoffice/2013/07/19/remarks-president-trayvon-martin.
One paper asked See “Are Emily and Greg More Employable Than Lakisha and
Jamal? A Field Experiment on Labor Market Discrimination,” by Marianne
Bertrand and Sendhil Mullainathan, American Economic Review 94, no. 4
(2004): 991–1013, doi: 10.1257/0002828042002561.
Osagie K. Obasogie My discussion of Obasogie’s work relies on Francie
Latour’s Boston Globe article “How Blind People See Race,” January 19,
2014. Latour provides a précis of Obasogie’s book Blinded by Sight: Seeing
Race Through the Eyes of the Blind (Redwood City, CA: Stanford University
Press, 2014), and interviews him.
Baywatch I was in Japan in 1992. Baywatch was popular worldwide by then, but
didn’t arrive in the Japanese mainstream until a year later. Nonetheless, surf
culture, California, and sun-kissed blondness were already everywhere.
When you walked into a “cool” clothing store, they’d be playing the Beach
Boys. In 1992. Stuff like “Surfin’ Safari,” not “Kokomo.”
Chapter 7: The Beauty Myth in Apotheosis
Korean proverb I got this from William Manchester’s biography of Douglas
MacArthur, American Caesar (New York: Little, Brown, 1978), which, in
the death throes of this book, I was reading to get my mind off data.
beauty operates on a Richter scale I was already familiar with the logarithmic
nature of the Richter scale, but relied on the Wikipedia entry for “Richter
magnitude scale” to understand the implications of the benchmark
magnitudes. In comparing beauty to the scale, I am, of course, employing a
bit of poetic license; the functions are not exactly the same.
Here is data for interview requests The Shiftgig data was provided by their
data team and with the gracious cooperation of founder Eddie Lou.
And for friend counts These are the aggregated and anonymized friend counts
for OkCupid users who’ve elected to connect their OkCupid accounts to their
Facebook accounts.
a foundational paper of social psychology See “What Is Beautiful Is Good,”
by Karen Dion, Ellen Berscheid, and Elaine Walster in Journal of
Personality and Social Psychology 24 (1972): 285–90.
It was the first in a now long line … This passage adapts conclusions from
and directly quotes “Pretty Smart? Why We Equate Beauty with Truth,” by
Robert M. Sapolsky, in the Wall Street Journal, January 17, 2014. The Duke
neuropsychologists alluded to are Takashi Tsukiura and Roberto Cabeza. See
also “Jurors Biased in Sentencing Decisions by the Attractiveness of the
Defendant” at Psychology and Crime News for an overview of the effects of
physical attractiveness in the criminal justice process: crimepsychblog.com/?
p=1437, posted by user EmmaB, April 3, 2007.
both Tumblr and Pinterest See “A New Policy Against Self-Harm Blogs,”
Tumblr’s staff blog, March 1, 2012,
staff.tumblr.com/post/18132624829/self-harm-blogs.
See also “Pinterest ‘Thinspiration’ Content Banned According to New
Acceptable Use Policy,” by Ellie Krupnick, Huffington Post, March 26,
2012, huffingtonpost.com/2012/03/26/pinterest-thinspiration-contentbanned_n_1380484.html.
The Huffington Post has actively covered the “thinspiration” phenomenon.
See “The Hunger Blogs: A Secret World of Teenage ‘Thinspiration,’ ” by
Carolyn Gregoire, February 8, 2012,
huffingtonpost.com/2012/02/08/thinspiration-blogs_n_1264459.html.
For more on “thighgap” (and for evidence that altering the Terms of
Service did not solve the problem), see “The Sexualization of the Thigh
Gap,” by Allie Jones, on The Wire, November 22, 2013,
thewire.com/culture/2013/11/sexualization-thigh-gap/355434/.
Chapter 8: It’s What’s Inside That Counts
That’s been the popular standard since These basic facts on the origins of
Gallup were found on the “Gallup (company)” Wikipedia entry.
surveys have historically As I mention in the text and in the footnotes to this
chapter, the idea of using Google Trends to look at taboos is the brainchild of
Seth Stephens-Davidowitz. His June 9, 2012, article in the New York Times,
“How Racist Are We? Ask Google,” and his 2013 Harvard PhD dissertation,
“Essays Using Google Data,” http://nrs.harvard.edu/urn3:HUL.InstRepos:10984881, were the inspiration for this chapter. For the
question of exactly how much Obama’s race cost him in the 2008 election,
picked up later in the chapter, I rely directly on Stephens-Davidowitz’s work.
For the over-time use of the word “nigger” and in the other direct citations of
Google Trends findings in the chapter, the work is my own, though I am
adapting a method he first suggested.
Though Stephens-Davidowitz now works at Google, I emphasize that his
search research is always based on public and anonymous sources, not on
privileged access to anyone’s personal search history. My own search
research is similarly based on a public, anonymous source, namely Google
Trends: google.com/trends.
This tendency is called I used Wikipedia’s “Social desirability bias” entry as
my source for basic details here.
The most famous case The Bradley effect first came to my attention during the
2008 campaign, as pundits wondered how it would affect Obama’s polling
on Election Day. Here, I relied on the Wikipedia entry “Bradley effect” for
basic facts surrounding Tom Bradley’s defeat.
Since the service launched See Nick Bilton, “Google Search Terms Can
Predict Stock Market, Study Finds,” New York Times Bits blog, April 26,
2013. See also Casey Johnston, “Google Trends Reveals Clues About the
Mentality of Richer Nations,” Arstechnica, April 5, 2012,
arstechnica.com/gadgets/2012/04/google-trends-reveals-clues-about-thementality-of-richer-nations/; and Tobias Preis et al., “Quantifying the
Advantage of Looking Forward,” Scientific Reports 2, no. 350 (2012), doi:
10.1038/srep00350.
track epidemics of flu Google Flu was first developed in the paper “Detecting
Influenza Epidemics Using Search Engine Query Data,” by Jeremy Ginsberg
et al. in Nature 457 (2009): 1012–14, doi:10.1038/nature07634. Recently,
Flu’s efficacy has been found wanting: see Kaiser Fung, “Google Flu
Trends’ Failure Shows Good Data > Big Data,” Harvard Business Review
Blog Network, March 25, 2014.
included in 7 million searches a year Stephens-Davidowitz, “How Racist Are
We?”
more American than “apple pie” Google Trends index for US searches,
January 2004–September 2013, for “apple pie”: 25. For “nigger”: 32.
And, tellingly The ratio of “nigga”:“nigger” is thirty times higher in tweets
sent from my Twitter corpus than reflected in Google Trends. That is, on
Twitter “nigger” appears thirty times less frequently.
roughly 1 in 100 searches for “Obama” Stephens-Davidowitz shared this fact
with me over e-mail.
25 percent below the pre-Obama status quo Stephens-Davidowitz, “How Racist
Are We?” This is also confirmable firsthand through Google Trends.
Other awful terms These racial epithets are far less common on Twitter, in
private messages to OkCupid, and in Google search, as confirmed by
Stephens-Davidowitz via e-mail.
If you’re not familiar with autocomplete The algorithm that supplies Google
autocomplete is the blackest of the black boxes. There is little definitive
information on how it works. Danny Sullivan at searchengineland.com offers
a thorough, if mostly ad hoc, overview at searchengineland.com/howgoogle-instant-autocomplete-suggestions-work-62592. Because
autocomplete seems to factor in your personal search history, individual
results are highly variable here. If you try to replicate my results for yourself,
make sure to use an “Incognito” session of Chrome, as I did, so that Google
has no prior personal data to work with. If you’re a Safari user, select
“Private Browsing.”
one such result See Paul Baker and Amanda Potts, “ ‘Why Do White People
Have Thin Lips?’ Google and the Perpetuation of Stereotypes Via AutoComplete Search Forms,” Critical Discourse Studies 10, no. 2 (2013): 187–
204.
Go to your search bar with This long string of queries was suggested to me by
Sean Mathey, on the van ride home following a camping trip where we
played a lot of Magic: the Gathering.
I’ll let Republican strategist Lee Atwater explain See Rick Perlstein,
“Exclusive: Lee Atwater’s Infamous 1981 Interview on the Southern
Strategy,” The Nation, November 13, 2012,
thenation.com/article/170841/exclusive-lee-atwaters-infamous-1981-
interview-southern-strategy. Original quote from Alexander P. Lamis’s book
The Two-Party South (New York: Oxford University Press, 1984), via
Wikiquote’s “Lee Atwater” entry.
Consider two media markets Stephens-Davidowitz, “How Racist Are We?”
In my opinion, Muhammad Ali I read David Remnick’s King of the World (New
York: Random House, 1998) in 1999 and have admired Ali since. I verified
certain basic facts surrounding Ali’s Vietnam protest using his Wikipedia
entry. For Ali’s famous quote on the Viet Cong, I went with the popular and
much more pithy misquotation of his actual words, which were, “My
conscience won’t let me go shoot my brother, or some darker people, or
some poor hungry people in the mud for big powerful America. And shoot
them for what? They never called me nigger, they never lynched me, they
didn’t put no dogs on me, they didn’t rob me of my nationality, rape and kill
my mother and father … Shoot them for what? How can I shoot them poor
people? Just take me to jail.” The misquotation is identical in spirit, yet so
much shorter and so much better known, that I decided it was acceptable in
place of the actual quote.
You can hear him say those words (the longer quote) himself in the
YouTube video “Muhammad Ali on the Vietnam War-Draft” at
https://www.youtube.com/watch?v=HeFMyrWlZ68. In that video, he seems
to be speaking right after a fight, and his speech is slow and deliberate. Hear
him speak much more fluently on the same topic two years later in
“Muhammad Ali Interview with Ian Wooldridge (1969)” at
https://www.youtube.com/watch?v=dLam_GiQ2Ww.
Chapter 9: Days of Rage
Safiyyah Nawaz tweeted a silly joke My sources for information on Safiyyah
and for the tweets surrounding her ordeal were:
Neetzan Zimmerman, “Teen Posts Joke on Twitter, Internet Orders Her to
Kill Herself,” Gawker, January 2, 2013, gawker.com/1493156583.
Ryan Broderick, “Meet the 17-Year-Old Girl Who Stood Up to Death
Threats After Her Tweet Went Viral on New Year’s Eve,” BuzzFeed,
January 2, 2014, buzzfeed.com/ryanhatesthis/meet-the-17-year-old-girlwho-stood-up-to-death-threats-afte.
Ryan Broderick, “After Twitter Started Viciously Attacking Her over a Silly
Joke, This Girl Handled It Like a Champ,” BuzzFeed, January 2, 2014,
buzzfeed.com/ryanhatesthis/after-twitter-started-attacking-her-over-asilly-joke-this-g.
These articles put her retweet number at 14,000, but they were all
published just a day later. My 16,000 was accurate as of mid-January 2014.
Katy Perry/Lady Gaga The counts of the retweets for their “Happy New Year”
tweets were accurate as of mid-January 2014 and have most likely gone up
somewhat in the time since.
comedian Natasha Leggero My sources for Leggero’s joke and the subsequent
uproar were:
“ ‘I’m Not Sorry’: Comedian Natasha Leggero Refuses to Apologize
Mocking Pearl Harbor Survivors on NBC,” by that legendary gumshoe
“DAILY MAIL REPORTER.” Mail Online, January 4, 2014,
dailymail.co.uk/news/article-2533809/.
Ross Luippold, “Natasha Leggero’s Stunning ‘Not Sorry’ Response over
Controversial Pearl Harbor Joke,” Huffington Post, January 4, 2014,
huffingtonpost.com/2014/01/04/natasha-leggero-not-sorry-for-pearlharbor-joke_n_4541354.html.
The derogatory tweets sent to Leggero were taken from a letter she
published on her Tumblr: natashaleggero.com/letter/.
Pictures of her family Justine’s tweet and the outrage surrounding it were
covered extensively. A decent overview of the uproar is here: “Justine Sacco:
5 Fast Facts You Need to Know,” by Matthew Guariglia, on Heavy,
December 21, 2013, heavy.com/news/2013/12/justine-sacco-iac-racist-prtweet-africa/.
“This Is How a Woman’s Offensive Tweet Became the World’s Top
Story,” by Alison Vingiano, on BuzzFeed, is a more thorough survey, though
one that conveniently omits BuzzFeed’s own role in cheering on the mob:
buzzfeed.com/alisonvingiano/this-is-how-a-womans-offensive-tweetbecame-the-worlds-top-s.
“The Case of Justine Sacco and the Twitter Lynch Mob,” by Sharon
Waxman, in The Wrap, is a piece by someone who, like me, had worked
with Justine: thewrap.com/case-justine-sacco-twitter-lynch-mob/.
“Justine Sacco: How to Kill a Career with One Tweet,” by Juana Poareo,
is one of many pitiless articles, replete with screenshots of Justine’s tweets in
the aftermath. The Guardian, “Liberty Voice,” December 22, 2013,
guardianlv.com/2013/12/justine-sacco-how-to-kill-a-career-with-one-tweet/.
A screenshot of Google’s involvement in #HasJustineLandedYet can be
found at “Justine Sacco Saga Sparks Criticism of Twitter Lynch Mob,” by
Lauren O’Neil, on CBCnews.com:
cbc.ca/newsblogs/yourcommunity/2013/12/justine-sacco-saga-sparkscriticism-of-twitter-lynch-mob.html.
the Internet waited dry-mouthed Here, though there were many thousands of
mean-spirited tweets to choose from in my data pull, I chose to print only
tweets that had already been published by other sources:
@RonGeraci’s tweet appears on his blog, The Minty Plum, in a thoughtful
piece, “View from the Pitchfork Mob,” January 12, 2014, the
mintyplum.com/?p=486.
@noyokono’s tweet appears in Frazier Tharpe, “PR Woman Tweets
Racist Joke Before Flight, Twitter Waits for Her to Land and Get Fired,”
Complex.com, December 21, 2013, complex.com/popculture/2013/12/justine-sacco-racist-tweet/.
@Kennymack1971’s tweet appears in the Sharon Waxman article cited
above, “The Case of Justine Sacco and the Twitter Lynch Mob.”
her father isn’t a billionaire Alec Hogg, “Rubbish Rumours. Tweeting Idiot
Justine Sacco No Relation to Desmond Sacco, SA Mining Billionaire,” Biz
News.com, December 27, 2013, biznews.com/tweeting-idiot-justine-sacco-
no-relation-to-desmond-sacco-sa-mining-billionaire/.
The reach of social media This research did not use our usual randomized
Twitter corpus. We instead opted for a completist approach. For these
numbers and the related chart, my team and I pulled every retweet of
Safiyyah’s joke and #HasJustineLandedYet. These numbers reflect our best
estimates of who saw each.
Marine biologists Alan Yu, “More Than 300 Sharks in Australia Are Now on
Twitter,” All Tech Considered, December 31, 2013, NPR,
npr.org/blogs/alltechconsidered/2013/12/31/258670211/.
Rumors are mentioned My source for the history and science of rumors is
Jesse Singal’s piece “How to Fight a Rumor,” Boston Globe, October 12,
2008,
boston.com/bostonglobe/ideas/articles/2008/10/12/how_to_fight_a_rumor/.
The insight to connect rumors and social media virulence is his. He also
quotes the “a man who lacks judgment …” passage from the Bible. “Judge
not …” is my own addition, as is the “demon Rumor.”
I also used “Rumor, Gossip and Urban Legends,” by Nicholas DiFonzo
and Prashant Bordia, in Diogenes 54, no. 1 (2007): 19–35, and Mr.
DiFonzo’s article “Rumour Research Can Douse Digital Wildfires” in
Nature 493, no. 7431 (2013): 135.
a phenomenon first studied I was led to Suler’s work from Penny Arcade. I
drew basic facts on Suler and the online disinhibition effect from the
Wikipedia entry for “Online disinhibition effect,” which links to the comic.
The comic itself is here: penny-arcade.com/comic/2004/03/19.
The old CB radio channels I became aware of this fact through the Wikipedia
entry for “Online disinhibition effect,” which cites Kenneth Tynan, “Fifteen
Years of the Salto Mortale,” The New Yorker, February 20, 1978, as the
original source.
the Jerky Boys For anyone interested in the world of phone-call humor,
Longmont Potion Castle is the Mitch Hedberg to the Jerky Boys’ Dane
Cook. I could never recommend the Longmont Potion Castle II album
strongly enough.
People still flame one another See Todd Dugdale, “Sandbaggers and Trolls,”
kd0tls Ham Radio Experience, January 6, 2014,
kd0tls.blogspot.com/2014/01/sandbaggers-and-trolls.html/.
The government has the greatest vested My discussion of government
surveillance of unrest, and the work of Peter Gloor at MIT, draws from
“What Makes Heroic Strife,” Economist, April 21, 2012,
economist.com/node/21553006/.
27.5 percent of Twitter’s 500 million tweets This number is from analysis of my
randomized research sample.
Facebook’s data team Facebook’s data analysis is always done with
anonymized and aggregated data. This discussion of iterations surrounding
the “No one should …” meme, and the attendant table, was drawn from Lada
Adamic et al., “The Evolution of Memes on Facebook,” January 18, 2014,
facebook.com/notes/facebook-data-science/the-evolution-of-memes-onfacebook/10151988334203859. The post leaves it unclear how political bias
was determined. My best guess is from users’ “like” patterns.
1In 1950 This paragraph discussing polarization in American politics is based
on Jill Lepore, “Long Division,” The New Yorker, December 2, 2013.
“It has always been a mystery” I read Life of Mahatma Gandhi by Louis Fisher
(New York: Harper & Brothers, 1950) in 2007, and this quote has stuck with
me since.
Chapter 10: Tall for an Asian
To find out what’s actually special to a particular group The method for
reducing a group’s collected profile text to the idiosyncratic essentials I
present in this chapter is my own. However, the OkCupid blog post that
inspired this work—“The Real Stuff White People Like”—used a different
method, developed with help from Max Shron and Aditya Mukerjee. I would
not have developed my own method in this book without their prior example
for that post. I developed my own method because the one used for that post
had me sorting the nonsense from the “real data” as the final step. For this
book, I wanted something completely algorithmic, where no human selection
came into play. The method is as described—you plot the words and phrases
on the grid by their percentiles and then rank them by their Euclidean
distance from the desired corner of the square.
The human element came into play only in the few cases where redundant
phrases, such as “my blue eyes and,” “blue eyes and,” and “my blue eyes”
appeared on the list together. In those cases, I took the most representative
word or phrase and deleted the others. The lists were not meaningfully
altered by this. The method considered all phrases of four words or fewer
that appeared in more than thirty profiles.
Because of space considerations three lengthy entries were pared down to
avoid line wrapping. In the male antithesis table I used “follow me” instead
of “follow me on instagram.” In the female antithesis, I used “malcolm x”
instead of “biography of malcolm x,” and in the words by orientation table in
the next chapter I used “feminine women” instead of “attracted to feminine
women.”
something called Zipf’s law I was familiar with power law distributions
already. However, I used the “Zipf’s law” Wikipedia page for more
information on the law. “Zipf’s Law and Vocabulary,” by C. Joseph Sorell,
The Encyclopedia of Applied Linguistics, November 5, 2012, was also a
resource. The table in the text was excerpted from a longer table presented in
that paper.
The Irish and eastern Europeans From Nell Irvin Painter’s The History of
White People (New York: W. W. Norton, 2010).
in Mexico I lived in Mexico for several years as a child and have retained an
interest in its politics. See Ronald Loewe, Maya or Mestizo?: Nationalism,
Modernity, and Its Discontents (Toronto: University of Toronto Press, 2010).
“From empathy and sexuality” See Bobbi J. Carothers and Harry T. Reis,
“Men and Women Are from Earth: Examining the Latent Structure of
Gender,” Journal of Personality and Social Psychology 104, no. 2 (2013):
385–407. “Men Are from Mars Earth, Women Are from Venus Earth” is the
title of the article’s précis:
sciencedaily.com/releases/2013/02/130204094518.htm.
Aristotle looked to the emptiness I was already familiar with the heavens’ role
in Einstein’s and Newton’s work. For the third, older, example, I hunted
around Wikipedia until I found an example I liked. See the entry for “Aether
(classical element).”
Chapter 11: Ever Fallen in Love?
A few years ago a couple of MIT students Here, I used “Project ‘Gaydar,’ ”
by Carolyn Y. Johnson, Boston Globe, September 20, 2009, and the students’
original paper, “Gaydar: Facebook Friendships Expose Sexual Orientation”
by Carter Jernigan and Behram F. T. Mistree, First Monday 14, no. 10
(2009), firstmonday.org/article/view/2611/2302.
The Kinsey Report in 1948 See Wikipedia’s “Kinsey Reports” entry, which
summarizes the male and female editions of Kinsey’s work. The 10 percent
number for men is straightforward. There is less certainty in the report
around women’s sexuality. The report says 2 to 6 percent of females aged
twenty to thirty-five are “exclusively” homosexual.
Later studies See Wikipedia’s “Demographics of sexual orientation” for all
kinds of numbers. Also see “LGBT demographics of the United States.”
“This work can usefully” Dan Black et al., “Demographics of the Gay and
Lesbian Population in the United States: Evidence from Available
Systematic Data Sources,” Demography 37, no. 2 (2000): 139–54.
This surely involves a painful choice See Assi Azar, “Op-ed: To You There,
in the Closet,” The Advocate, April 16, 2013,
advocate.com/commentary/2013/04/16/op-ed-you-there-closet/.
no more unusual than naturally blond hair My source is Professor C. George
Boeree, of Shippensburg University. See his post “Race” at web
space.ship.edu/cgboer/race.html. Even back-of-the-envelope math proves his
point: there are roughly 1 billion Europeans, Canadians, Americans, and
Australians on Earth. If 1 in 6 of them is naturally blond, which in my
personal circle would be a vast overestimate, that’s 2 percent of the world
right there.
According to Stephens-Davidowitz My four-page discussion of gay porn
searches and their implications adapts findings from Stephens-Davidowitz’s
piece “How Many American Men Are Gay?” New York Times, December 7,
2013. Both the Google Trends data I cite and its extension to Nate Silver’s
findings and to Gallup’s state-by-state numbers are based on that article.
Silver’s original piece is “How Opinion on Same-Sex Marriage Is Changing,
and What It Means,” from his New York Times fivethirtyeight blog,
fivethirtyeight.blogs.nytimes.com/2013/03/26/how-opinion-on-same-sexmarriage-is-changing-and-what-it-means/.
Gallup’s numbers are from Gary J. Gates and Frank Newport, “LGBT
Percentage Highest in D.C., Lowest in North Dakota,”
gallup.com/poll/160517/lgbt-percentage-highest-lowest-north-dakota.aspx.
so does mobility data from Facebook In his article, Stephens-Davidowitz also
extended his research into publicly available Facebook profile data.
often attributed to Thoreau The quote itself is a combination of a passage in
Thoreau’s Walden with two lines of Oliver Wendell Holmes’s poem “The
Voiceless.” See The Walden Woods Project:
walden.org/Library/Quotations/The_Henry_D._Thoreau_MisQuotation_Page.
The old economic “misery index” is See Wikipedia’s “Misery index
(economics).” Arthur Okun suggested the original formulation.
“Respondents who identified” See Mackey Friedman, “Considerable Gender,
Racial and Sexuality Differences Exist in Attitudes Toward Bisexuality,”
ScienceDaily, November 5, 2013,
sciencedaily.com/releases/2013/11/131105081521.htm.
Gerulf Rieger of the University of Essex I reference a pair of papers by Professor
Rieger and his team: Gerulf Rieger, Meredith L. Chivers, and J. Michael
Bailey, “Sexual Arousal Patterns of Bisexual Men,” Psychological Science
16, no. 8 (2005): 579–84, and its successor, Gerulf Reiger et al., “Male
Bisexual Arousal: A Matter of Curiosity?,” Biological Psychology 94, no. 3
(2013): 479–89.
Ellyn Ruthstrom See David Tuller, “No Surprise for Bisexual Men: Report
Indicates They Exist,” New York Times, August 22, 2011, and Meredith
Melnick, “Scientific Study Finds That Bisexuality Really Exists,” Time,
August 23, 2011, healthland.time.com/2011/08/23/scientific-study-findsthat-bisexuality-really-exists/.
On Facebook 58 percent See Chris Taylor, “Fake Facebook Users Likely to Be
Popular Bisexual College Women,” Mashable, February 3, 2012,
mashable.com/2012/02/03/fake-facebook-users-bisexual-college-women/.
Though people have been gay forever See Wikipedia’s “Timeline of LGBT
history” and “Coming out” entries. The idea of self-disclosure (that is,
coming out) as an act of empowerment was originated by Karl Heinrich
Ulrichs.
Chapter 12: Know Your Place
The United States and the USSR split Korea I was generally familiar with this
process, mostly from American Caesar, but this incredible anecdote is
mentioned on the “Division of Korea” Wikipedia entry, which cites Don
Oberdorfer’s book The Two Koreas (New York: Basic Books, 2001) as the
original source. I confirmed the anecdote via a search on the book’s text on
Google Books: books.google.com/books/about/The_Two_Koreas.html?
id=yJZKpYXh2SAC.
Here you see a plot This map, like all the full US maps in this chapter, and the
Reddit plot, was made by James Dowdell. This one was made using a
standard Voronoi partition of the United States, which each Craigslist market
serving as the “capital” of a “state” (called “seeds” and “cells”). Though the
plot looks complex, it’s actually very elegant: the segments are all the points
equidistant to the two nearest seeds. I’ve seen various other versions of this
same plot. My version was inspired by one made by IDV Solutions and
posted by “john.nelson” on their UX blog:
uxblog.idvsolutions.com/2011/07/chalkboard-maps-united-states-of.html.
venue of longing is Walmart This is the same Voronoi plot, but combined with
the by-state data from Dorothy Gambrell’s “Missed Connections” map,
published in Psychology Today. The cells are coded by the top missedconnection result for the state where their seed lies. You can see the original
map here: psychologytoday.com/blog/brainstorm/201302/missedconnections-0.
I transported the data to the previous Voronoi partition in order to
maintain consistency with the previous Craigslist map.
Years ago, an enterprising hacker The hacker is Pete Warden, and his post is
“How to Split Up the US,” which you can find here:
petewarden.com/2010/02/06/how-to-split-up-the-us/. As Warden notes in a
later post, “Why You Should Never Trust a Data Scientist,” his grouping of
the United States into the seven new zones is arbitrary—the data science
version of “for entertainment purposes only.” I reference them here in that
spirit.
Matthew Zook, a geographer Professor Zook and his team maintain a fantastic
geography blog called Floating Sheep, and that blog was my primary source
for his work: floatingsheep.org.
The earthquake discussion and the map are drawn from “Mapping the
Eastern Kentucky Earthquake” posted on the Floating Sheep blog by Taylor
Shelton. My image is a reproduction of the original, simplified for print:
floatingsheep.org/2012/11/mapping-eastern-kentucky-earthquake.html.
The DOLLY team is Matthew Zook, Mark Graham, Taylor Shelton,
Monica Stephens, and Ate Poorthuis. Poorthuis narrates the Sint Maarten
walkthrough, which can be found here: www.youtube.com/watch?
v=pD9HWAaQGUA.
My discussion of the student riot is drawn from the paper “Beyond the
Geotag: Situating ‘Big Data’ and Leveraging the Potential of the Geoweb,”
by Jeremy W. Crampton et al., Cartography and Geographic Information
Science 40, no. 2 (2013): 130–39.
Below is a plot of gay porn downloads IP address does not pinpoint any one
person (or, more precisely, a computer address) to their exact location, only
to a range of about ten to fifty miles. It is roughly the same technology used
by, say, weather.com, to guess at what city’s weather to show you by default
before you tell it a zip code. It only tells the general area from which a
computer is accessing the Internet. From this research, we know nothing
about the computers themselves other than what porn they were
downloading; and we know absolutely nothing about who was actually using
the computer, or in some cases, if there was even a person involved at all.
a forty-year-old woman in the Bay Area See “I’m Just Gonna Throw This Out
There. Any Redditors in the SF Bay Area Have a Empty Spot at Their Table
for a Lonely Thanksgiving Orphan?” posted by user “MeMyselfOhMy” on
Reddit: reddit.com/r/AskReddit/comments/ebhh1/.
topics that you’ll only find on Reddit The example posts mentioned were all
on the front page of their respective subreddits on January 30, 2013.
Anderson’s main topics are nationalism Showing the flexibility of his theory,
many of Anderson’s ideas on nationhood are surprisingly applicable to
online communities. He describes nations as “both inherently limited and
sovereign” and “conceived as a deep, horizontal comradeship.” And
especially applicable to the Internet is this passage: “This new synchronic
novelty could arise historically only when substantial groups of people were
in a position to think of themselves as living lives parallel to those of other
substantial groups of people—if never meeting, yet certainly proceeding
along the same trajectory.” Benedict Anderson, Imagined Communities
(London: Verso, 1983), 6, 191–92.
a worldwide look at modern large-scale movements I obtained permission from
the Facebook researchers Aude Hofleitner, Ta Virot Chiraphadhanakul, and
Bogdan State to reproduce their map and discuss their results. They asked
that I include a more robust explanation of “coordinated migration” and of
their study. Here are their words:
In a coordinated migration, a significant proportion of the population of
a city has migrated, as a group, to a different city. More specifically, a
flow of population from city A (hometown) to another city B (current
city) is considered a coordinated migration if, among the cities in which
people from hometown A currently live, city B is the city with the
largest number of individuals with current city B, and hometown A.
There are numerous migrations to, from, and within the United States
but they do not exhibit this coordinated property because there is no
overly dominant attractive city and people move to different areas. This
map displays chunks of the small towns and villages of Southeast Asia
relocating en masse, in a coordinated fashion, to the urban centers.
For more information and the full study, please refer to the Facebook Data
Science post on Coordinated Migration: www.facebook.com/notes/facebookdata-science/coordinated-migration/10151930946453859.
As you’ll see when you visit the link, in reproducing their work, I
modified their original map by removing the labels and focusing on a smaller
part of the region, to make the map more readable in print. Thank you to
Mike Develin, also at Facebook, for helping facilitate permission for this
reproduction. All Facebook Data Science work is done on anonymized and
aggregated data.
Chapter 13: Our Brand Could Be Your Life
But what they don’t tell you See Clare Baker, “Behind the Red Triangle: The
Bass Pale Ale Brand and Logo” Logoworks.com, November 8, 2013,
logoworks.com/blog/bass-pale-ale-brand-and-logo/.
Archaeologists have unearthed My discussion of branding in ancient times is
based on David Wengrow, “Prehistories of Commodity Branding,” Current
Anthropology 49, no. 1 (2008): 7–34, and Gary Richardson, “Brand Names
Before the Industrial Revolution,” NBER Working Paper No. 13930,
National Bureau of Economic Research, Cambridge, MA, 2008.
http://papers.nber.org/paper/w10411.
In 1997, Tom Peters See “The Brand Called You” by Tom Peters, published
in Fast Company, August/September 1997, fastcompany.com/28905/brandcalled-you.
still read in marketing classes See “What a great article. I was given this to
read for a class of mine, and it is written brilliantly. Great insight and
information on branding. Thanks!!” a comment by user “Morgan” on Peter’s
article on Fastcompany.com.
a man named Peter Montoya Montoya’s first work on the topic was titled The
Brand Called You: The Ultimate Brand-Building and Business Development
Handbook to Transform Anyone into an Indispensable Personal Brand, by
Peter Montoya and Tim Vandehey (self-published, 2003). This was then
republished as The Brand Called You: Make Your Business Stand Out in a
Crowded Marketplace (New York: McGraw-Hill, 2008), which according to
Amazon was an “international bestseller.” A PDF of the first chapter is
hosted here: petermontoya.com/pdfs/tbcy-chapter1.pdf. Montoya’s personal
site redirects to marketinglibrary.net, where you can book him for speaking
engagements.
You can see the birth of the idea For this chart, I subtracted the long-standing
idiom of “personal brand of” (as in “personal brand of leadership”) from the
results for “personal brand” to isolate the self-marketing phenomenon.
Dale Carnegie I relied on Wikipedia’s “Dale Carnegie” entry for basic details
on his life.
For every kid who tweets herself The two incidents I allude to here are Bernie
Zak’s campaign to get into UCLA, as detailed in Brock Parker, “Brookline
Student Lobbies UCLA on Twitter” Boston Globe, May 7, 2013, and Rob
Meyer’s hiring by the Atlantic Monthly, as described in Alexis C. Madrigal,
“How to Actually Get a Job on Twitter,” Atlantic Monthly, July 31, 2013.
See also Jason Fagone, “The Construction of a Twitter Aesthetic,” The
New Yorker, February 12, 2014,
newyorker.com/online/blogs/culture/2014/02/the-construction-of-a-twitteraesthetic.html.
the different way African Americans tend My discussion of Black Twitter
drew on the following sources:
Choire Sicha, “What Were Black People Talking About on Twitter Last
Night?” The Awl, November 11, 2009, theawl.com/2009/11/what-wereblack-people-talking-about-on-twitter-last-night.
Farhad Manjoo, “How Black People Use Twitter,” Slate, August 10, 2010,
slate.com/articles/technology/technology/2010/08/how_black_people_use_twitter.html
A counterpoint to Manjoo’s piece is “Why ‘They’ Don’t Understand What
Black People Do on Twitter” by Dr. Goddess, on blogspot. Goddess
especially objects to the portrayal of blacks on Twitter as a “monolith”—
the word appears twice in the post, and I echo it in my discussion. See
drgoddess.blogspot.com/2010/08/why-they-dont-understand-whatblack.html.
“How to Be Black Online,” a slideshow by Baratunde Thurston, is a clever
overview of Black Twitter and acknowledges better than most sources
that, like many racial tropes, “Black Twitter” is both “funny because it’s
true” and inaccurate at the same time. See slideshare.net/baratunde/howto-be-black-online-by-baratunde.
Hard data on Twitter usage by ethnicity can be found in the Pew Research
report “Demographics of Key Social Networking Platforms” (2013), by
Maeve Duggan and Aaron Smith:
pewinternet.org/2013/12/30/demographics-of-key-social-networkingplatforms/.
For evidence of white confusion over Black Twitter, see Nick Douglas,
“Micah’s ‘Black People on Twitter’ Theory,” Too Much Nick, August 21,
2009, toomuchnick.com/post/168222309/.
Right now there are 2,643 The site Social Bakers ranks all Twitter accounts by
number of followers. The number has, no doubt, changed. Visit
socialbakers.com/twitter/ and page back through the rankings to see for
yourself. For information on US taxpayers by income, visit the IRS’s “SOI
Tax Stats—Individual Statistical Tables by Filing Status” page at
irs.gov/uac/SOI-Tax-Stats---Individual-Statistical-Tables-by-Filing-Status.
Information on the Forbes Billionaires list is from Elizabeth Barber, “Forbes’
Richest People: Number of Billionaires up Significantly,” Christian Science
Monitor, March 3, 2014, csmonitor.com/USA/USAUpdate/2014/0303/Forbes-richest-people-number-of-billionaires-upsignificantly-video.
Newt Gingrich boasted See Jeff Neumann, “Newt Gingrich Brags About His
Twitter Followers,” Gawker, August 1, 2011, gawker.com/5826477/. Also
see John Cook, “Update: Only 92% of Newt Gingrich’s Twitter Followers
Are Fake,” Gawker, August 2, 2011, gawker.com/5826960/.
Mitt Romney See “Is Mitt Romney Buying Twitter Followers?” by Zach Green
on 140elect: 140elect.com/twitter-politics/is-mitt-romney-buying-twitterfollowers/. My data and chart are adapted from the data and chart in that
post.
“We, the users” See Jenna Wortham, “Valley of the Blahs: How Justin Bieber’s
Troubles Exposed Twitter’s Achilles’ Heel,” New York Times Bits blog,
January 25, 2014, bits.blogs.nytimes.com/2014/01/25/valley-of-the-blahshow-justin-biebers-downfall-exposed-twitters-achilles-heel/.
In 2012, Salesforce.com My discussion of Salesforce’s job post draws on the
following sources:
Drew Olanoff, “Klout Would Like Potential Employers to Consider Your
Score Before Hiring You. And That’s Stupid,” TechCrunch, September
29, 2012, techcrunch.com/2012/09/29/klout-would-like-potentialemployers-to-consider-your-score-before-hiring-you-and-thats-stupid/.
Jessica Roy, “Want to Work at Salesforce? Better Have a Klout Score of 35
or Higher,” BetaBeat, September 27, 2012, betabeat.com/2012/09/youmay-not-work-at-salesforce-unless-you-have-a-klout-score-of-35-orhigher/.
The original job posting was still active when I was writing, but has since
been removed.
The gates open and close See Larry Wissel, “How Does a Logic Gate in a
Microchip Work? A Gate Seems Like a Device That Must Swing Open and
Closed, Yet Microchips Are Etched onto Silicon Wafers That Have No
Moving Parts. So How Can the Gate Open and Close?” Scientific American,
“Ask the Experts,” October 21, 1999, scientificamerican.com/article/howdoes-a-logic-gate-in/.
The gates on a microchip aren’t doors in the traditional sense, swinging on
tiny hinges. They use voltage to control movement, whereas an old gate
might use wooden slats. But they, like gates, control flow from one space to
another, and are either open or shut.
Target, by analyzing a customer’s purchases See Kashmir Hill, “How Target
Figured Out a Teen Girl Was Pregnant Before Her Father Did,” Forbes,
February 16, 2012, forbes.com/sites/kashmirhill/2012/02/16/how-targetfigured-out-a-teen-girl-was-pregnant-before-her-father-did/.
a Jell-O marketing campaign The Jell-O discussion and illustrative tweets are
drawn from Harry Bradford, “Jell-O’s Fun My Life Twitter Campaign:
Social Media Genius or Just ‘Funning’ Annoying?” Huffington Post, May
24, 2013, huffingtonpost.com/2013/05/24/jello-fun-my-lifetwitter_n_3332230.html.
McDonald’s sent out Drawn from Hannah Roberts, “#McFail! McDonalds’
Twitter Promotion Backfires as Users Hijack #Mcdstories Hashtag to Share
Fast Food Horror Stories,” Daily Mail, January 24, 2012,
dailymail.co.uk/news/article-2090862/.
Wendy’s had tried Drawn from “When Twitter Hashtag Promotion Marketing
Goes Bad #HeresTheBeef” by blogger “stacie,” on the Divine Miss Mommy
blog: thedivinemissmommy.com/when-twitter-hashtag-promotionmarketing-goes-bad-heresthebeef/.
More recently, Mountain Dew See Everett Rosenfeld, “Mountain Dew’s ‘Dub
the Dew’ Online Poll Goes Horribly Wrong,” Time, August 14, 2012,
newsfeed.time.com/2012/08/14/mountain-dews-dub-the-dew-online-pollgoes-horribly-wrong/.
Chapter 14: Breadcrumbs
As of May 2013, Facebook was recording See Craig Smith, “By the Numbers:
98 Amazing Facebook Stats,” Digital Marketing Ramblings, March 13,
2014, expandedramblings.com/index.php/by-the-numbers-17-amazingfacebook-stats/#.U1AArPldXko.
a group from the UK This passage and the table are based on “Private Traits
and Attributes Are Predictable from Digital Records of Human Behavior,”
by Michal Kosinskia, David Stillwell, and Thore Graepel, Proceedings of the
National Academy of Sciences 110, no. 15 (2013): 5802–5805.
Xbox One See Stephen Fairclough, “Physiological Data Must Remain
Confidential,” Nature 505, no. 7483 (2014): 263.
The UK has 5.9 million See David Barrett, “One Surveillance Camera for
Every 11 People in Britain, Says CCTV Survey,” Telegraph, July 10, 2013,
telegraph.co.uk/technology/10172298/.
In Manhattan See Brian Palmer, “Big Apple Is Watching You,” Slate, May 3,
2010,
slate.com/articles/news_and_politics/explainer/2010/05/big_apple_is_watching_you.htmlAll those security cameras See Jon Healey, “Surveillance Cameras and the
Boston Marathon Bombing,” Los Angeles Times, April 17, 2013,
articles.latimes.com/2013/apr/17/news/la-ol-boston-bombing-surveillancesuspects-20130417.
See also “The Need for Closed Circuit Television in Mass Transit,” by
Michael Greenberger, University of Maryland Legal Studies Research Paper
No. 2006–15, Law Enforcement Executive Forum (2006): 151, digital
commons.law.umaryland.edu/cgi/viewcontentcgi?
article=1065&context=fac_pubs.
“master the Internet” This phrase in particular refers to the NSA’s cooperation
with the surveillance apparatuses of other governments, as part of the “Five
Eyes” Alliance. See Wikipedia’s “Mastering the internet” entry. The slide
depicted was widely circulated after its publication by the Guardian. See
theguardian.com/world/interactive/2013/nov/01/prism-slides-nsa-document.
“For each of the millions” See David Medine et al., “Report on the Telephone
Records Program Conducted under Section 215 of the USA PATRIOT Act
and on the Operations of the Foreign Intelligence Surveillance Court,”
Privacy and Civil Liberties Oversight Board (2014),
http://www.fas.org/irp/offdocs/pclob-215.pdf.
Women are using apps My discussion of menstruation apps is based on Jenna
Wortham, “Our Bodies, Our Apps: For the Love of Period-Trackers,” New
York Times, January 23, 2014.
there’s a startup that says it can infer This fact is from Jaron Lanier, “How
Should We Think About Privacy?” Scientific American, November 2013,
65–71.
all the analysis was done anonymously and in aggregate It bears repeating that
at no time was any data tied back to any individual. For the user photos and
text cited in the book see the notes above related to them.
Jaron Lanier My discussion of Lanier’s work focuses on his article “How
Should We Think About Privacy?”
“Using data drawn from queries” See John Markoff, “Unreported Side Effects
of Drugs Are Found Using Internet Search Data, Study Finds,” New York
Times, March 7, 2013, nytimes.com/2013/03/07/science/unreported-sideeffects-of-drugs-found-using-internet-data-study-finds.html.
a crowdsourced family tree Geni.com reports more than 75 million entries in its
tree. They’re owned by MyHeritage, which claims 1.5 billion.
two political scientists debunked See Jowei Chen and Jonathan Rodden, “Don’t
Blame the Maps,” New York Times, January 26, 2014,
nytimes.com/2014/01/26/opinion/sunday/its-the-geography-stupid.html.
Facebook was collecting 500 terabytes See Eliza Kern, “Facebook Is Collecting
Your Data—500 Terabytes a Day,” Gigaom, August 22, 2012,
gigaom.com/2012/08/22/facebook-is-collecting-your-data-500-terabytes-aday/.
Alex Pentland at MIT My discussion of Pentland draws on his article “Reality
Mining of Mobile Communications: Toward a New Deal on Data,” in Global
Information Technology Report 2008–2009, ed. Soumitra Dutta and Irene
Mia (Geneva: World Economic Forum, 2009), 75–80, and an interview with
him, “An Interview with Alex ‘Sandy’ Pentland About ‘Social Physics’ ” by
IDcubed: idcubed.org/?post_type=home_page_feature&p=880.
The Washington Post captures the shortfall See “Million Mask March
descends on Washington” on the Washington Post’s PostTV blog:
http://wapo.st/1b5Kt5J.
Coda
Tufte’s books The discussion of the Vietnam Memorial, and the quote I use,
are from Beautiful Evidence (Cheshire, CT: Graphics Press, 2006), but
Tufte’s Envisioning Information (Cheshire, CT: Graphics Press, 1990) and
The Visual Display of Quantitative Information (Cheshire, CT: Graphics
Press, 2001) were also indispensible.
The memorial was digitized in 2008 See fold3.com/thewall and Mallory
Simon, “Vets Pay Tribute to Fallen Comrades at Virtual Vietnam Wall,”
CNN.com, April 1, 2008, cnn.com/2008/TECH/04/01/vietnam.wall/.
Two pictures had been added to his entry PFC Wilson’s profile on fold3 is at
fold3.com/page/631972608_lorne_john_wilson/stories/. It is unclear if he is
personally depicted in the group picture. It’s clearly an authentic snapshot
from the Vietnam War, but it is blurry.
Acknowledgments
Like pages without binding, this project and indeed my life would’ve flown to
the winds long ago without my wife, Reshma. Thank you for your unwavering
support, selflessness, and love.
Thank you to Max Krohn, Sam Yagan, and Chris Coyne for building OkCupid
and for having me along. It has been a privilege to work with and for you guys
for the last fifteen years.
Thank you to my agent, Chris Parris-Lamb, who turned Dataclysm from a
rambling pitch at a bar into a bonafide proposal, and to Amanda Cook, my editor
at Crown, who took it from there. To the extent this book is a success, her
patience and skill have made mere ideas into something worth reading. Thank
you also to Emma Berry, editorial assistant, and to the design team, especially
Chris Brand, for bringing Dataclysm into being, and to Annsley Rosner, Sarah
Breivogel, Sarah Pekdemir, and Jay Sones for helping it out into the world. The
support and vision of Molly Stern, Jacob Lewis, and David Drake made all of
the above possible. Thank you, too, to Allison Lorentzen at Penguin for her very
early guidance into the publishing world.
Thank you to James Dowdell, my versatile data researcher and programmer.
James did the essential database work behind Dataclysm and also generated
many of the book’s maps and network plots. Thank you to Tom Quisel and Mike
Maxim for pulling (and repulling!) data from OkCupid, and for being excellent
sounding boards for my various statistical ideas.
Thank you to my parents and my sister for their encouragement and for being
the foundation of my life. Thank you to the Patel family for supporting me and,
especially, Reshma, while we bent our days and weeks and months around
getting this book finished.
Thank you to Eddie Lou at Shiftgig, Tim Abraham at StumbleUpon (and now
Twitter), Ryan Ogle and Sean Rad at Tinder, Jim Talbot at Match, Tom Jacques
at Datehookup, and Erik Martin at Reddit for aggregated data and access. Thank
you to Michael Tapper and Ben Murray for reading drafts, and to Sean Mathey
at Mathey & Tree, Eric Brown at Franklin, Weinrib, Rudell & Vassallo, and
John Therien at Smith Anderson for legal work. Thank you to Doug Demay for
advice that was no less wise for being informal. Finally, thank you to Jed
McCaleb and Justin Rice, who, from d20s to bitcoin to Dylan to Ulysses, have
taught me so much. My life and this book are much richer for your friendship.
Index
Page numbers in italics refer to illustrations.
Abrams, J. J.
abstractions, 3.1, 4.1, 4.2, 12.1
Academy of Motion Picture Arts and Sciences
Africa, 9.1, 9.2n, 9.3, 12.1
African Americans, 12.1, nts.1
jokes about, 8.1n, 8.2, 9.1
as political candidates, 8.1, 8.2
on Twitter, 13.1, nts.1
see also racism
aging
AIDS, 9.1, 9.2
algorithms, itr.1, 4.1, 6.1, 9.1, 9.2, 10.1n, 10.2, 10.3, 10.4, 11.1, 12.1, 13.1, 13.2,
14.1, 14.2, 14.3
Ali, Muhammad, 8.1, nts.1
Amazon, itr.1, bm2.1, nts.1
American Institute of Public Opinion
American Political Science Association (APSA)
Anderson, Benedict, 12.1, nts.1
Anderson, Pamela
Anonymous collective
anorexia
Apple, 3.1, 14.1
apps, itr.1, 3.1, 4.1, 5.1, 12.1, 13.1, 14.1, nts.1
Arab Spring, n
Aristotle, 10.1, nts.1
Arizona State University, 3.1, nts.1
Asians, itr.1, 6.1, 8.1, 10.1
atheism, 5.1, 7.1
attractiveness, 5.1, 5.2, 6.1, 7.1, nts.1, nts.2
aging and
disparities in, 5.1, 5.2, 5.3
jobs and, 7.1, 7.2
of men to women, 1.1, 2.1, 5.1, 6.1
race and, 6.1, 6.2
satisfaction and, 5.1, 5.2, 5.3
sex and, itr.1, 1.1, 2.1, 6.1, 7.1, 7.2, bm2.1
of women to men, 1.1, 5.1
Atwater, Lee, 8.1, nts.1
Backstrom, Lars, 4.1, nts.1, nts.2
ballads, 3.1, 10.1
Ballou, Sullivan, 3.1, 3.2, nts.1, nts.2
Bass Ale, 13.1, 13.2, nts.1
Baywatch (TV series), 6.1, nts.1
Beatles, 10.1, 13.1
beauty, itr.1, 1.1, 4.1, 5.1, 6.1, 7.1n
definition of, 2.1, nts.1
divisiveness of, itr.1, 7.1
effects of
imperfection and, 2.1, 2.2
Beauty Myth, The (Wolf)
behavior research
Big Data, itr.1, itr.2, 6.1
Big Lead, The (blog)
biology:
evolutionary
marine, 9.1, nts.1
bisexuality, itr.1, 11.1n, nts.1
male vs. female, 11.1, 11.2, 11.3
message exchanges and, 11.1, 11.2
vocabulary typical of, 11.1
Bisexual Resource Center
blindness, 6.1, nts.1
blogs, itr.1, 3.1, 5.1, 6.1, 13.1, bm2.1
body-image
Blumenbach, Johann
books, 3.1n, 3.2, 8.1, 12.1
Boston, Mass., 6.1, 11.1
Boston Globe, 6.1, 9.1, 11.1, nts.1, nts.2, nts.3
Boston Marathon bombing, 14.1, nts.1
Bradley effect, 8.1, nts.1
brain, itr.1, 2.1, 7.1
Brand Called You, The (Montoya)
“Brand Called You, The” (Peters)
brands, 9.1, 13.1, nts.1
personal, 13.1, 13.2, nts.1
product, 13.1, 13.2, nts.1, nts.2
Breitbart, Andrew
British Trademark Registration Act
Bujalski, Andrew
Burns, Ken
BuzzFeed, 9.1, nts.1
calculus, 4.1, 9.1
California, 8.1, 12.1, 12.2, 12.3
cancer
Carnegie, Andrew
Carnegie, Dale, 13.1, 13.2, nts.1
Carver, Raymond
celebrities, 9.1, 11.1, 13.1, 14.1
gay
Census, US, 1.1, 10.1, nts.1, nts.2
Centers for Disease Control (CDC)
Chicago, Ill., 8.1, 12.1, 12.2
children, itr.1, 11.1, 11.2, 12.1, bm2.1
birth of
raising of, 1.1, 2.1, 7.1
teenage, 1.1, 2.1, 3.1, 7.1, 7.2, 9.1, 10.1, 12.1, 13.1
China
Christianity, 7.1, 13.1
Chungking Express (film)
Civil War, The (TV series)
Civil War, US, 3.1, 3.2
Clinton, Hillary Rodham
Clovis people
Coldest Winter Ever, The (Sister Souljah)
Columbia University
communication, 3.1, 5.1, 9.1, 13.1, 14.1
connections fostered by, 3.1, 3.2, 3.3, 13.1
identifying sources of
momentous changes in, 3.1, 3.2
communities, itr.1, 12.1, 13.1
movement of
virtual
Computer Fraud and Abuse Act (CFAA)
computers, itr.1, itr.2, 5.1, 6.1, 8.1, 13.1
cookies on
hard drives on
laptop, itr.1, 13.1
limitations of
science of, 4.1, 13.1, 14.1
sitting at
software for, 4.1, 4.2, 6.1, 9.1, 11.1, 12.1, 14.1, 14.2
storage of data on, itr.1, 1.1, 3.1, 14.1
use of mouse with
Condor, 9.1, 9.2
Congress, US, 9.1, 12.1
approval ratings of, itr.1, nts.1
see also House of Representatives, US
Constitute project
conversation, itr.1, 4.1, 7.1, 8.1
in-depth
on-line, 5.1, 5.2, 5.3, 5.4
on race
Cornell University, 11.1, 12.1
Craigslist, itr.1, 12.1, nts.1
maps of, 12.1, 12.2, 12.3
“Missed Connections” section on, 12.1, 12.2
Crawford, Cindy
Crick, Francis
criminal justice system, 6.1, 7.1
black vs. white defendants in, 6.1, 8.1
Cronkite, Walter
cross dressing
Cuban Missile Crisis
culturomics, 3.1, 3.2n
curves, itr.1, itr.2, 7.1, 7.2, 9.1, bm2.1, nts.1
bell
beta, itr.1, nts.1
customer relations management (CRM)
customers
contradictory behavior of
Cyrus, Miley
data, itr.1, 9.1, bm2.1
actor vs. acted upon in
analysis of, itr.1, 1.1, 2.1, 4.1, 6.1, 14.1, bm2.1
collection of, itr.1, itr.2, itr.3, itr.4, itr.5, 1.1, 1.2, 8.1, 12.1, 14.1
commercial use of, itr.1, 14.1, 14.2
corporate use of, 14.1, 14.2, 14.3
cross-referencing of
deletion of, 14.1, 14.2
digital, itr.1, itr.2, itr.3, 6.1, bm2.1
emotional shading behind
extrapolations from, 6.1, 8.1, 14.1
governmental surveillance of, itr.1, 14.1, 14.2, 14.3, nts.1
hacking of, 12.1, 14.1, 14.2, nts.1
of human interaction, itr.1, itr.2
human story behind, itr.1, itr.2
lack of
location
longitudinal
message, 3.1, 6.1
personal
pollution of, 11.1n, 12.1
privacy issue and, itr.1, 14.1, 14.2, 14.3, 14.4, nts.1
robust, 5.1, 10.1
selection bias and
selling of, 14.1, 14.2
storage of, itr.1, 1.1, 3.1, 14.1
as storytelling
terabytes of, itr.1, 2.1
truth of, 13.1, 14.1
unprecedented deluge of, itr.1, 4.1, 14.1, 14.2
use of color with, itr.1, 3.1, bm1.1
visualization of, 1.1n, 14.1
as windows on our lives
databases, itr.1, 1.1, 3.1, 8.1
dataclysm.org/relationshiptest, 4.1
DateHookup, itr.1, 6.1, 6.2
dating, 1.1, 3.1, 4.1, 5.1, nts.1
attractiveness and satisfaction in, 5.1, 5.2, 5.3
racism and, 6.1, 6.2, 6.3, 6.4, 6.5
see also websites, dating
Dazed and Confused (film), 1.1, 12.1
death
“and taxes,”
death penalty
Democratic Party, 5.1, 8.1, 13.1
demographics, itr.1, 1.1, 5.1, 6.1, 6.2, 10.1
depression, 8.1, 11.1
Description of a Slave Ship
Digital OnLine Life and You (DOLLY Project), 12.1, nts.1
disease, 4.1, 14.1, bm1.1
epidemics of, 8.1, 8.2, 14.1, nts.1
“Dittoheads,”
dogfooding
Don’t Look Back (film)
Dowdell, James, 4.1, nts.1
drugs, 8.1, 11.1
side effects of
Dylan, Bob, itr.1, itr.2
Earth, itr.1, 2.1, 10.1, 14.1, 14.2, 14.3
age of, 9.1, 9.2
as viewed from space
earthquakes, 7.1, 12.1, 12.2, nts.1
eating disorders
economics, 1.1, 8.1, 13.1
Economist, 9.1, nts.1
education, 1.1, 5.1, 6.1
college, itr.1, 4.1, 6.1, 10.1, 13.1, 14.1
exchange programs in
high school, itr.1, 3.1, 6.1, 9.1, 12.1, 13.1
Egypt, 9.1, 9.2, 13.1
Einstein, Albert, 10.1, 13.1, 13.2, 14.1
elections, US
black candidates in, 8.1, 8.2
district gerrymandering and
exit polls in
of 1952
of 1982
of 2008, 8.1, 8.2, nts.1, nts.2
of 2012
e-mail, 3.1, 3.2, 4.1, 5.1, 12.1, 14.1
embeddedness, 4.1, 4.2
employment, 6.1, 6.2
search for, 7.1, 7.2
see also jobs
English language, 3.1, 3.2, 10.1
Enlightenment era, 4.1, 6.1
Escher, M. C.
Essex, University of, 11.1, nts.1
Euler, Leonhard, 4.1, 4.2, 10.1n, nts.1
evolution, 2.1, 9.1
Exif
eyes
blue, 10.1, nts.1
Facebook, itr.1, itr.2, itr.3, 4.1, 6.1, 7.1, 9.1, 11.1, 12.1, 13.1, 14.1, nts.1
data collection of, itr.1, itr.2, itr.3, 4.1, 4.2, 6.1, 11.1, 12.1, 14.1, 14.2, bm2.1,
nts.1
Data Science team of, 12.1, nts.1
declining use of, 3.1, nts.1
fake profiles on
friends on, itr.1, itr.2, 4.1, 4.2, 4.3, 7.1, 7.2, 9.1
“like” button on, itr.1, 6.1, 9.1, 14.1, 14.2
married people on
racial content of
Terms of Service on, 14.1, bm2.1, nts.1
Timeline on
worldwide use of, itr.1, nts.1
fame
families, 4.1, 6.1, 7.1, 11.1, 14.1
Fast Company
Feynman, Richard
Fiennes, Ralph, 3.1, 3.2, nts.1
films, 4.1, 9.1, 14.1
documentary
favorite
scary
financial market volatility, 2.1, 2.2
Fitbits
flag burning, 12.1, 12.2
flaws, 2.1, 2.2, 6.1, 14.1
Fleming, Alexander
flirting, 3.1, 12.1
Forbes, 13.1n, 13.2
forced school busing
Ford Motors
Foursquare, itr.1, nts.1
friends, itr.1, itr.2, 3.1, 4.1, 4.2, 12.1
best
black
college
counting of
loss of
mutual, 4.1, 4.2
work
see also Facebook, friends on
Frosty (dog)
FuelBand
future, 1.1, 14.1
fear of
Gaga, Lady, 9.1, nts.1
Gallup polls, itr.1, 8.1, 11.1, 11.2, nts.1
Gandhi, Mohandas K. “Mahatma,” 9.1, nts.1
Gawker, 9.1, 13.1n, nts.1
“gaydar,” 11.1, 14.1, nts.1
General Relativity theory
genes, 3.1, 4.1, 14.1
geni.com, 14.1, nts.1
geometry, 1.1, 1.2, 10.1
Gingrich, Newt, 13.1, nts.1
Gladiator (film)
Gladwell, Malcolm
Global Positioning System (GPS), 12.1, 14.1
Gloor, Peter, 9.1, nts.1
Gmail
“God,” n
Google, itr.1, itr.2, 3.1, 6.1, 12.1, 13.1, 14.1, 14.2, 14.3, bm2.1, nts.1, nts.2
autocomplete function of, 8.1, 10.1, bm2.1, nts.1
data collection of, itr.1, 14.1
percentage of Americans on
vilest impulses unleashed on, 8.1, 8.2
visual design team at, 5.1, nts.1
Google Books, 3.1, 3.2n, 8.1, 9.1, 10.1, 13.1, 13.2, nts.1
Google Flu, 8.1, 14.1, nts.1
Google Search, itr.1, 7.1, 8.1, 9.1, 11.1, 11.2
frequency of the word “nigger” on, itr.1, 8.1, 8.2, 8.3
Google Trends, 8.1, 8.2, 10.1, 11.1, 12.1, nts.1, nts.2
Gorbachev, Mikhail
gossip
graphic designers, 5.1, 5.2
Greek mythology, 1.1, 9.1
Grindr, n
Guardian, 14.1, 14.2, nts.1
guitars, 13.1, bm2.1
Gujarati language
hair:
Caucasian
color of, 2.1, 6.1, 7.1n, nts.1
Halpern, Justin
Hamlet (Shakespeare)
“Happy Birthday,” bm2.1, bm2.2
Harper’s
Harvard University
hate speech, 8.1, 9.1
health-care debates
heart, 2.1, 8.1
monitoring of, n
Hendrix, Jimi
high school yearbooks
Hipstamatic
History of White People, The (Painter), 6.1, nts.1
Hitler, Adolf
homosexuality, itr.1, 8.1, 11.1, bm2.1, nts.1
“closeted,”
experiences of, 11.1, 11.2
genetic causes of
gradual acceptance of, itr.1, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6
population estimates of, 11.1, 11.2, 11.3
promiscuous
self-disclosure of, 11.1, 11.2, 11.3, 11.4, 11.5
universality of
vocabulary typical of, 11.1
see also bisexuality; lesbianism; marriage, gay
House of Representatives, US
Houston, Tex., 6.1, 10.1
Huffington Post, 12.1, nts.1, nts.2
human genome
human spirit
hyperglycemia
hypertext markup language (HTML)
ideas
gap between
intuitive, 4.1, 6.1
new, 1.1, 4.1, 13.1
Imagined Communities (Anderson)
India, 12.1, 13.1
indole, 2.1, nts.1
Industrial Revolution
industry
Accounts Receivable in
automotive
service, 5.1, 7.1
see also jobs
inequality, 6.1, 11.1
influenza, 8.1, nts.1
information
absence of
design of
making choices from
nonsense vs.
personally identifiable (PII), 14.1, 14.2
sharing of
innovation, 4.1, 13.1, 14.1
Instagram, itr.1, 1.1, 3.1, 13.1, 14.1
instant messaging (IM)
intelligence, itr.1, 6.1, 7.1, nts.1
testing of
InterActiveCorp (IAC)
Internet, itr.1, itr.2, 1.1, 2.1, 3.1, 6.1, 7.1, 12.1, 13.1, 13.2, 13.3, 14.1, 14.2, nts.1
cultural impact of, 3.1, 5.1
democratization process on, itr.1, 12.1n
demographics and use of, itr.1, 6.1, 6.2, nts.1
era before, 6.1, 12.1
hate speech on, 8.1, 9.1
human interaction on
maintaining privacy on, itr.1, 14.1, 14.2, nts.1
making of public figures on
negativity on
percentage of Americans on
wide audience provided by
Internet Protocol (IP)
intuition, 4.1, 6.1
iPhone, 14.1, 14.2, bm2.1
iPhoto
Irish people, 10.1, nts.1
Japan
JavaScript, 13.1, 14.1
Jell-O, 13.1, nts.1
jobs, itr.1, 4.1, 11.1, 13.1, 13.2
coworkers at, 4.1, 4.2, 4.3
government
loss of
male vs. female interviews for, 7.1, 7.2
promise of
workplace performance on
see also employment
Jobs, Steve, itr.1, 5.1, nts.1
journalism, 3.1, 14.1
Joyce, James
Justice Department, US
Justin (friend), itr.1, 2.1
Kentucky
Kerry, John
keyboards, itr.1, 3.1
King Kong (film)
Kinsey Report, The (Kinsey), 11.1, nts.1
Kleinberg, Jon, 4.1, nts.1, nts.2
Klout, 13.1, 14.1, bm2.1, nts.1
Königsberg, bridges of, 4.1, nts.1
Korean peninsula, 7.1, nts.1, nts.2
38th parallel in
Ku Klux Klan (KKK)
Lamis, Alexander P., 8.1, nts.1
language, 3.1, 9.1, 11.1, 12.1
dialects of, 3.1, 10.1
literary, 3.1, 3.2, 3.3, 10.1
programming
study of
on Twitter, 3.1, 13.1, nts.1
variety and preservation of
Lanier, Jaron, 14.1, 14.2, 14.3, nts.1
Lee, Spike
Leggero, Natasha, 9.1, 9.2, 12.1, nts.1
Lennon, John, itr.1, 13.1
lesbianism, 11.1, 11.2
Liberman, Mark, 3.1, nts.1
Limbaugh, Rush
LinkedIn, 5.1, 7.1
Linklater, Richard
LOLs, 9.1, 9.2
London subway bombings
Louisiana, 11.1, 12.1
love, itr.1, 3.1, 4.1
dating sites and
“experts” on
lust vs., 12.1, 12.2
Lucas, George
lust, itr.1, 11.1
love vs., 12.1, 12.2
McCain, John
McCartney, Paul, itr.1, 13.1, bm2.1
McConaughey, Matthew
McDonald’s, 13.1, nts.1
Maddow, Rachel
Mandela, Nelson
Manjoo, Farhad, 13.1, nts.1
mapmaking, 12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, nts.1
marriage, itr.1, 1.1, 4.1, 4.2, 5.1, 9.1
civil unions vs., 11.1
evaluation of
gay, itr.1, 11.1, 11.2, 11.3, 11.4
sexless
Mars/Venus metaphor, 10.1, nts.1
Martin, Trayvon
Massachusetts Institute of Technology (MIT), 9.1, 11.1, 14.1, 14.2, nts.1, nts.2
masturbation fantasy
Match.com, itr.1, 6.1, 6.2, 6.3, 7.1
mathematics, itr.1, 1.1, 2.1, 2.2, 2.3, 6.1, 7.1, 9.1, 10.1n, 10.2, 11.1, 13.1, 13.2,
14.1
measurement, 5.1, 7.1
memory, 1.1, bm1.1
men:
aging of, 1.1, 1.2, 1.3, 1.4
Asian, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 10.1, 10.2
attraction of women to, 1.1, 1.2, 1.3
attractiveness of women to, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 2.1, 2.2, 2.3
black, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 8.1, 8.2, 10.1
contacting of women by, 1.1, 1.2, 1.3, 1.4, 2.1, 3.1
Latino, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 10.1, 10.2, 10.3
sexual aims of
straight, 11.1, bm2.1
white, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6,
10.7
Mexico, 10.1, nts.1
Michel, Jean-Baptiste, 3.1n, nts.1
microchips, 13.1, nts.1
Microsoft, 4.1, 14.1, 14.2, 14.3, nts.1
Microsoft Research
Milgram, Stanley, 4.1, 4.2, nts.1
“misery index,” 11.1, nts.1
Mississippi
models, professional
Montoya, Peter, 13.1, 13.2, nts.1
Morris, Errol
Mountain Dew, 13.1, nts.1
Murakami, Haruki
music, 2.1, 4.1, 10.1, 13.1
identification of
taste in, 1.1, 1.2
Myers-Briggs test
Myspace
names, ethnicity of
Napoleon I, Emperor of France, 8.1, bm1.1
Narmer, Pharoah, itr.1, nts.1
Nas, 8.1, 8.2
National Basketball Association (NBA), itr.1, itr.2, nts.1
National Football League (NFL)
National Geographic
National Security Agency (NSA), 14.1, 14.2, 14.3
PRISM program of, 14.1, 14.2, 14.3
Nature, 14.1n, nts.1
Nawaz, Safiyya, 9.1, 9.2, 9.3, 12.1, nts.1
network analysis
network theory, 4.1, nts.1
Newton, Isaac, 10.1, 14.1
New York, NY, 6.1, 11.1, 12.1, 12.2
Brooklyn, 8.1, 12.1
Manhattan, 12.1, 12.2, 14.1, nts.1
people watching in
Queens
Rockefeller Center
Times Square
Upper East Side
New Yorker, 13.1, nts.1, nts.2
New York Times, itr.1, 11.1, 11.2, 13.1, 14.1, 14.2, nts.1, nts.2, nts.3
OpEd page of
Nielsen ratings
“nigga,” 8.1n, 8.2, nts.1
“nigger,” 8.1, 8.2, 8.3, 8.4, 8.5, 9.1, nts.1
Nigger (recording), 8.1, 8.2
Niggerhead Lake
Nixon, Richard M.
North Dakota, 11.1, 11.2
Norwegian Wood (Murakami)
nouns, 3.1, 3.2
Obama, Barack, 9.1, 13.1, nts.1
“change” byword of
on racism
2008 election of, 8.1, 8.2, 9.1, nts.1, nts.2
2009 inauguration of, 8.1, 8.2
2012 election of
Obasogie, Osagie K., 6.1, nts.1
Occupy! movement
OkCupid, itr.1, itr.2, itr.3, itr.4, 2.1, 7.1, 7.2, 9.1, 12.1, 13.1
accounts removed from
apps of, 3.1, 5.1
attractiveness ratings on, 7.1, 7.2, 7.3, 7.4
compatibility (match percentage) ratings on, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8
Crazy Blind Date app promoted by, 5.1, 6.1, nts.1
data collection of, itr.1, 1.1, 1.2, 2.1, 5.1, 6.1, 6.2, 11.1, 12.1, bm2.1
founding of, itr.1, itr.2
gay users of, 11.1, 11.2, 11.3, 11.4
as largest dating website, itr.1, nts.1
Love Is Blind Day on, 5.1, 5.2, 7.1
match questions on, 8.1, 11.1, 11.2, 14.1
median age of users of
messages exchanged on, 3.1, 5.1, 5.2, 5.3, 5.4, 7.1, 7.2, 7.3, 7.4, 13.1, bm2.1,
nts.1
personal profiles on, itr.1, 2.1, 2.2, 6.1, 6.2, 10.1, 10.2, 11.1, 11.2, 11.3
photographs on, 6.1, 7.1, 7.2, 7.3, 14.1, nts.1
racial composition of users of, 6.1, 6.2, 6.3, 8.1
racial data on, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9
Omen, The (film)
“online disinhibition effect,”
Oxford English Corpus (OEC), 3.1, 10.1, 10.2, nts.1
Painter, Nell Irvin, 6.1, nts.1
Palin, Sarah
paper
parents, 1.1, 2.1, 4.1, 4.2, 7.1, 11.1, 12.1, 14.1
Parsons code, bm2.1, bm2.2
Pearl Harbor attack
Pecker (film)
Penny Arcade, 9.1, nts.1
Pentland, Alex, 14.1, 14.2, nts.1
People’s History of the United States, A (Zinn)
Perry, Katy, 9.1, nts.1
Perry, Rick
personal essays
on OkCupid, itr.1, 2.1, 2.2, 6.1, 6.2, 10.1, 10.2, 11.1, 11.2, 11.3
Peters, Tom
Pew studies, itr.1, nts.1
Phil, Dr.
Phish, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6
photobombing
photographs, 5.1, 7.1, 9.1, 14.1, 14.2, bm1.1
captions of, 3.1, 5.1
on OkCupid, 6.1, 7.1, 7.2, 7.3, 14.1, nts.1
scrambled, 5.1, 5.2, 5.3
phrenologists
Pinterest, 7.1, 7.2, nts.1
Pitbull
Pixar, 4.1, nts.1
pixels
pizza, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, nts.1
planets, 10.1, nts.1
poetry, itr.1, 3.1, 11.1
polio vaccine
politics, 5.1, 5.2, 6.1, 7.1, 8.1, 11.1
gridlock in, 9.1, 14.1
liberal vs. conservative, 6.1, 9.1, 9.2
party, 5.1, 8.1, 8.2, 9.1, 14.1, nts.1
racism and
Twitter use and
popular culture, 3.1, 11.1
pornography
gay, 11.1, 12.1, nts.1, nts.2
women-with-women
Potomac River, 3.1, 3.2
PowerPoint presentations, 2.1, 14.1
“pratfall effect,” 2.1, nts.1
pravastatin
Privacy and Civil Liberties Oversight Board, US
psychology, 6.1, 10.1
neurosocial, 2.1, 7.1
punk rock
puns, 3.1, 6.1
Quantcast, 6.1, nts.1
Quantified Self movement
“Quantitative Analysis of Culture Using Millions of Digitized Books” (Michel
and Aiden), n
race, itr.1, itr.2, itr.3, 7.1, 8.1, 8.2
attractiveness and, 6.1, 6.2
four largest groupings by
Internet use and, itr.1, 6.1, 6.2
jokes about, 8.1n, 8.2, 9.1
quantitative analysis of, 6.1, nts.1
rhetoric about
tokenism and
racism, itr.1, 1.1, 5.1, 8.1, 9.1, 11.1
data on, itr.1, 6.1, nts.1
dating and, 6.1, 6.2
expression of, itr.1, 6.1, 8.1, 9.1, nts.1
Obama on
pervasiveness of, 6.1, 8.1
politics and
stereotypes of, 8.1, 10.1
radio
CB, 9.1, nts.1
ratings
compatibility, 6.1, 6.2, 6.3, 6.4
congressional, itr.1, itr.2
of men and women, itr.1, itr.2, itr.3, itr.4, itr.5, 1.1
pizza, itr.1, itr.2
Reagan, Ronald
Reddit, itr.1, itr.2, itr.3, 2.1, 12.1, 13.1, 14.1n, nts.1, nts.2
community and
subreddit pages on, 2.1n, 12.1
relationships
assimilated
bonds of, 4.1, 4.2, 4.3
breakup of, 1.1, 4.1
common interests in
connectors in, 4.1, 4.2
of couples, 1.1, 4.1, 5.1, bm2.1
courtship, 1.1, 4.1
evaluation of
family
leading separate lives in, 4.1, 4.2
progression of
“real life,”
romantic, 1.1, 2.1, 4.1, 4.2, 5.1, 6.1, 7.1, nts.1
stability in, itr.1, 4.1
see also dating; friends; marriage
Republican National Convention of 2008
Republican Party, 5.1, 8.1, 13.1, 14.1, nts.1
Richter scale, 7.1, 12.1, nts.1
Rieger, Gerulf, 11.1, nts.1
Romans, ancient
Romney, Mitt, Twitter followers of, 13.1, 13.2, nts.1
Rorschach tests
Rove, Karl
Russia, 9.1n, bm1.1
Ruthstrom, Ellyn, 11.1, nts.1
Sacco, Justine, 9.1, 9.2, 12.1, 13.1, nts.1
Salesforce.com, 13.1, 13.2, nts.1
Salk, Jonas
Samsung
Sapolsky, Robert, 7.1, nts.1
SAT
science, itr.1, 1.1, 2.1, 3.1, 6.1, 9.1
computer, 4.1, 13.1, 14.1
data, itr.1, itr.2, 2.1, 8.1n, 12.1, 12.2, 13.1, 14.1, 14.2, bm1.1, bm2.1, bm2.2
genetic
network analysis
political
social, itr.1, itr.2, 5.1, 6.1, 8.1, 9.1, 10.1, bm2.1
Scientific American, 14.1, 14.2, nts.1, nts.2
Scruff, n
Seacrest, Ryan
seismology, 7.1, 12.1
selfies
September 11, 2001, terrorist attacks
sex, itr.1, 1.1, 6.1, 8.1, 10.1, 11.1
attractiveness and, itr.1, 1.1, 2.1, 6.1, 7.1, 7.2, bm2.1
casual, 5.1, 11.1
regret and, itr.1, nts.1
threesome
see also bisexuality; homosexuality; lesbianism; lust
Shakespeare, William
Sharpton, Al
Shazam
Shiftgig, 7.1, 7.2, nts.1
showers, 12.1, 12.2
Silver, Nate, 11.1, 11.2, 14.1, nts.1
Simmons, Gene
“six degrees of separation” theory
Slackers (film)
Slate, itr.1n, 3.1, 13.1, nts.1
smartphones, itr.1, 12.1, 12.2
smell, sense of, 2.1, nts.1
Snapchat
Snowden, Edward, 14.1, 14.2
social desirability bias
social graphs, 4.1, 4.2, 4.3, 4.4
social media, 4.1, 6.1, 7.1, 9.1, 9.2, 13.1, 13.2, 14.1, nts.1
unrest and protest fanned on
social physics
solar eclipse of 1919
Sorell, C. Joseph, 10.1n, nts.1
Sparks, Nicholas
speech
hate, 8.1, 9.1
partisan
Spielberg, Steven
sports, 6.1, 8.1, 10.1, 12.1
Stanford-Binet test
states’ rights
statistics, itr.1, 6.1, 6.2, 10.1, 10.2
Stephens-Davidowitz, Seth, 8.1n, 8.2, 11.1, 11.2, bm2.1, nts.1, nts.2, nts.3
stock market predictions
Street Fighter II
string theory
Strunk, William
Suler, John
Supreme Court, US, 8.1, 13.1
symmetric beta distribution
Taboo (game)
talking points
Target, 13.1, nts.1
tattoos, 2.1, 2.2
taxation, 8.1, 14.1, 14.2
Tea Party, 8.1, 9.1
technology, itr.1, itr.2, 4.1, 5.1, 9.1, 12.1, 13.1, 14.1
cultural effect of, 3.1, 3.2, 9.1
harnessing of
telephones, itr.1, 3.1, 3.2, 3.3, 4.1, 4.2, 9.1
television, 6.1, 6.2, 14.1n
Tennyson, Alfred, Lord
terrorism
Texas, 8.1, 12.1, 12.2
text messages, 3.1, 3.2, 14.1
average length of, 3.1, 3.2, 3.3
copy-and-paste vs. from-scratch
keystrokes used on, 3.1, 3.2, 3.3
response rates to, 3.1, 3.2, 3.3
revision of
time spent on, 3.1, 3.2
Thoreau, Henry David, 11.1, nts.1
thought, 1.1, 8.1, 8.2
time, 3.1, 3.2, 8.1
passage of, 3.1, 3.2
spent on messages, 3.1, 3.2
Tinder, itr.1, 7.1
tribes, 3.1, 7.1, 7.2, 9.1, nts.1
Trump, Donald
Tufte, Edward R., bm1.1, nts.1
Tumblr, itr.1, 7.1, 9.1, 9.2, nts.1, nts.2
Clients from Hell posts on
Twitter, itr.1, itr.2, itr.3, itr.4, itr.5, 3.1, 3.2, 3.3, 4.1, 8.1, 9.1, 12.1, 12.2, 13.1,
nts.1
average word length on
black users of, 13.1, nts.1
common hashtags on, 13.1, 13.2, 13.3
followers on, 13.1, 13.2, 13.3
#HasJustineLandedYet topic on, 9.1, 9.2, nts.1
language style and vocabulary on, 3.1, 13.1, nts.1
messaging patterns of subgroups on
most common words on, 3.1, 10.1, 10.2
140-character limit on, 3.1, 3.2
TeamFollowBack on, 13.1, 13.2
Trending Topics list on
tweets and retweets on, itr.1, itr.2, 3.1, 3.2, 3.3, 6.1, 9.1, 9.2, 9.3, 9.4, 12.1,
12.2, 13.1, 13.2, 13.3, 13.4, nts.1
TwitterWind
ugliness, 1.1, 2.1, 6.1, 8.1
race and
social costs of
Ulysses (Joyce)
uniform resource locators (URLs), 2.1, 3.1n
Union of Soviet Socialist Republics (USSR), 12.1, nts.1
United Kingdom (UK), 6.1, 12.1, 13.1, 14.1, nts.1
United States, 6.1, 8.1, 8.2, 12.1
Internet usage in
moving in, 12.1, nts.1
national security apparatus of, itr.1, 14.1
Twitter use in
universal product code (UPC)
Utsunomiya
variance concept
verbs, 3.1, 3.2
Viet-Cong, 8.1, nts.1
Vietnam Memorial, bm1.1, nts.1
Vietnam War, 8.1, bm1.1, nts.1
visual perception, itr.1n, 6.1
Wall Street Journal, 7.1, nts.1
Walmart, 12.1, 12.2, 12.3, nts.1
Warden, Pete
Washington, DC, “Million” marches on, 14.1, nts.1
Washington Post, 14.1, 14.2, 14.3, nts.1
Waters, John, 2.1, 2.2, nts.1
Watson, James
wealth, 6.1, 7.1, 7.2n, 7.3, 11.1, 13.1
One Percent of
websites, itr.1, 4.1, 6.1, 12.1, 12.2
company
dating, itr.1, itr.2, itr.3, 1.1, 1.2, 2.1, 2.2, 3.1, 3.2, 4.1, 4.2, 5.1, 7.1, 12.1
job, itr.1, 7.1, 7.2
person-to-person interaction on, itr.1, itr.2, 2.1, 5.1, 6.1
ratings on, itr.1, itr.2, itr.3, itr.4, itr.5, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9,
2.1, 2.2, 6.1
social, itr.1, 4.1, 6.1
see also specific websites
WEIRD research, itr.1, 7.1n, nts.1
Wendy’s, 13.1, nts.1
“What Is Beautiful Is Good,” 7.1, nts.1
WhatsApp
WhoBeefed81
Who Owns the Future? (Lanier)
“Why Do White People Have Thin Lips?” (Baker and Potts), 8.1, nts.1
Wikipedia, 10.1, nts.1, nts.2
Wilson, Lorne John, bm1.1, nts.1
Windows, 4.1, nts.1
Wodehouse, P. G., 3.1, 3.2
Wolf, Naomi
women:
aging of, 1.1, 1.2, 1.3, 1.4
Asian, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2, 10.3
attraction of men to, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 2.1, 2.2, 2.3, 5.1, 5.2, 6.1,
6.2, 6.3, 6.4
attractiveness of men to, 1.1, 1.2, 1.3, 5.1, 5.2
black, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2
contacting of men by
dating pool of
extra pounds on
Latina, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2
men’s contacting of, 1.1, 1.2, 1.3, 1.4, 2.1, 3.1
menstrual cycles of
pregnancies of, 9.1, 13.1, 14.1, nts.1
straight, 11.1
unconventional-looking, 2.1, 2.2, 2.3
white, itr.1, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2
Wooderson’s law, 1.1, bm2.1
words:
antithetical, 10.1, 10.2, 10.3
changes in use of, 3.1, 3.2, 3.3
ethnic preferences for, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9
food, 3.1, 3.2
frequency of, 3.1, 3.2, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9
gender preferences for, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,
10.10
negative, 8.1, 9.1, 9.2
“netspeak,”
self-descriptive, 10.1, 10.2
shortening and contraction of, 3.1, 3.2, 3.3, 13.1
as social connectors, 3.1, 3.2
of Twitter users, 13.1
typing of, 3.1, 3.2, 3.3, 3.4, 3.5, 8.1
written
World War II
Wortham, Jenna, 13.1, 14.1, nts.1, nts.2
writing
changing culture of, itr.1, 3.1, 3.2, 3.3
Xbox One, 14.1, nts.1
Yahoo, 14.1, nts.1
Youth for Understanding program
YouTube, 3.1, 5.1, 12.1
Zimmerman, George, 8.1, nts.1
Zinn, Howard
Zipf’s law, 10.1, nts.1
Zipf’s Law and Vocabulary (Sorell), 10.1n
Zook, Matthew, 12.1, nts.1