Introductio n You have by now heard a lot about Big Data: che vase porential, the ominous consequences, the paradigm-descroying 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 iLSelf: the data, phenomenon stripped away. I come with a large store ofthe acmal informacion that's being collected, which luck. work. wheedling. and more luck have puc me in the unique position to possess and analyze. I was one of the founders of OkCupid, a dating website that, over a very un-bubbly long haul of ten years. has become one of the largest m the world. I started it with three friends. We were all mathematically minded, and the site succeeded in large pan because we applied that mind-sec co daring: we brought some analysts and rigor co what had histOrically been the domain oflove "expens" and grinning warlocks like Or. Phil. How chc sice works tsn'c all that sophisticated- it rums our the only math you need co model the process of two people getting to know each ocher is some sober arithmetic- but for whatever reason, our approach resonated, and this year alone 10 million people will use the site to fmd someone. As I know too well. websites (and founders of websites) love 10 throw out big numbers. and most thinking people have no doubt learned co tgnore them: you hear millions of this and billions of that and know it's basically "Hooray for me." said \vith cra!ling zeros. Unlike Google. Facebook, Twitter, and the ocher sources whose data \viii 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 l've thought a lm abour how to describe the reach of che site to someone who's never used It and who rightly doesn't care about the user-engagement mecrics of some guy's stanup. 111 put it in personal terms instead. Tonight, some thirty thousand couples will have their first date because 9 10 of OkCupid. Roughly three thousand of them will end up together long-term. Two hundred of those wtll get married, and many of them. of course, will have kids. Titere are children alive and pouting today. grouchy little humans refusing to put their shoes on rtghr 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 chat while I'm proud of the site my friends and l sraned. I honestly don't care If you're a member or go create an accoum 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. Istill subscribe co magazines. I gee the Tlmes on the weekend. Tweeting embarrasses me. I can't convince you to use. respect, or "belie~" the lmernet or social media any more than you already do-ordon't Byall 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 gee you to reconsider. it's what you chink about yourself. Because that's what this book is really about OkCupid is just how Iarrived at the story. Ihave led OkCupid's analytics team since 2009, and my job is co make sense of the data our users create. While my three founding parmers have done almost all the hard work of actually building the site. I've spem years just playing \vith the numbers. Some of what I work on helps us run the business: for example. understanding how men and women view sex and beauty differemly is essential for a daring site. But a lot of my results aren't directly useful- just interesting. There's nor much you can do with the fact thac. 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 ir at a diru1er party. That's basically all we did with chis stuff for a while; the insights we gleaned went no hmher than an occasional lame press release. But eventually we were analyzing enough information that larger trends became apparent. btg patterns in che small ones. and. even better. I realized I could use the data ro 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 me past. I could go and look ar what actually happens when, say. lOO,QCX) white Cataclysm men and lOO,QCX) black women interact In private.The data was sitting right there on our servers. It was an irresistible soctologtcal opportunity. I dug in, and as discoveries bulle up. like anyone with more ideas than audience. l sraned a blog to share them with the world. That blog then became this book. after one imponam improvemenL For Daraclysm, I've gone far beyond OkCupid. In face. I've probably pur together a data set of person-ro-person interaction that's deeper and more varied than anything held by any omcr private individual- spanning mosc. if noc all. ofrhe significant online data sources ofour time. ln these pages 111 use my data tO speak 110£ just tO me habits of one sire's users but also ro aset of universals. The public discussion of data has focused primarily on tvio things: govemmem spying and commercial opportunity. About the first, I doubt I know any more than you-only wbar I've read. To my knowledge. rhe national security apparatus has never approached any dating site for access. and unless they plan ro crirnlnalize the faceless display of utterly ripped abs or young women £rom Brooklyn going on and on abour how much they like scorch. when. come on. you know they really don't. Ican't imagine they'd find much ofinterest. About the second story. data-as-dollars. I know better. As I was beginning rhis book, the tech press was slick with drool over the Faccbook !PO: they'd collected everyone's personal dara 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. ATunes headline from three days before me offering says Itall: "Faccbook Must Spin Data imo Gold."You halfexpected Rumpelst~tskin 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 char 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. Bur awesome though the power may be, I'm noc 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 ro the data. I am going to put that user experience-the clicks. keystrokes. and m~liseconds-to another end. If Big Data's cwo running stories have been surveillance and money. for the last three years I've been working on a third: the human story. Introduction 11 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 lms of piaures with your ex. and stan 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. Twiuer. Reddit Tumblr, lnstagram. 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. dte data reveals how people behave when they think no one is watching. Here l \~show you what I've seen.Also, fuck body spray. 00 If you read a Joe 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 ma.kes for a curious absence in a story supposedly about people: there are very few individuals here. Graphs and chans and cables appear in abundance. but there are almost no names. lr's become a clicht of pop sc1cncc to use something small and quirky as a lens for big events-to tell the history of the world Via a rumip. to trace a war back to a fish. to shine a penlight through a prismjust so and cast the whole pretty rainbow on your bedroom wall. I'm going in the opposite direction. I'm caking something big-an enormous set of what people are doing and thinking and saying, terabytes of daca-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 lease likely to describe themselves, where and why gay people stay m the closet. how writing has changed in the last ten years. and how anger hasn't. The idea is to move our understanding ofourselves away from narratives and toward numbers. or, rather. co think in such a way that numbers are the narrative. This approach evolved from long toil in the statistical slag pits. Dataclysm 12 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 gee at their desires. habits, and revulsions. So you collect a lm ofdetailed data and work very hard to translate it all into general theories of human behavior. What a person develops working amidst all this infonnanon. as opposed to. say. working for the wedding section of the Sunday paper. is a special kinship w1th the shambling whole of humanity rather than with any £\VO 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 webslres. and indeed all data scientists. objectify. Algorithms don't work well with things that aren't numbers. so when you wam 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 lO chop and jam the continuum of human experience into lirde 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 rhe same lime you have to retain . as much of the je ne sais guoi 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 carrm sliced so cleanly that the pieces stay there In place on the cutting board. still in the shape of a carroL And while this tenslon-be£\veen 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 opporrunity: w put hard numbers to some timeless mysteries:to cake experiences chatwe've been content to puc aside as "unguanrifiable" and instead gam some understanding. As the approximations have gmten better and better. and as people have allowed them further into their lives, that understanding has Improved with scarding speed. I'm going to give you a guick example. but I first want ro say that "Making the Ineffable Tmally 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, websices askyou co vote because that vme curns something Ould and idiosyncratic- your opinioninto something they can understand and use. Dating Sites ask people to rate one anocher because it lets chem transform first Impressions such as: lnlroduc:ion 13 14 He's got beautiful eyes 1-Immm. he's cute, but I don't like redheads Ugh. gross ... into simple numbers. say. 5. 3, 1 on a five-scar scale. Sires have collected billions of these microjudgrnents, one person's snap opinion of someone else. Together. all those tiny thoughts form a source of vasr insight inro how people arrive at opmions of one anorher. 1l1e 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~thar with the average votes given to scraight women by straight men. This is the shape of the curve: 16- • women, as rated by men 12- %of 8- whole 4- o·-1.0 2.0 3.0 4.0 5.0 average received rating (on a 1· to S·S1ar scale) Fifty-one million preferences boil down ro this simple stand of recrangles. It is. in essence. the collected male opinion of female beauty on OkCupid. It folds all the tiny stories (what a man thinks ofa woman. millions of times over) and all the anecdotes (any one of which we could've expanded upon. were this a different kind ofbook) into an Intelligible whole. Looking at people like this is like looking at Earth from space; you lose the detail, but you get ro 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 ro one side or the other. Dataclysm When personal preference is mvolved. they often do. Take ratings of pizza joints on Foursquare, which rend to be very postrive: user ratings ofNew York City pizza places on Foursquare's 0- 10 scale 80- 60- number of pizza 40- places 20- 0 - 0 2 3 4 6 8 9 10 rating on Foursquare Or take the recent approval ratings for Congress. which, because politicians ·are the moral opposite of pizza, skew the other way: congressionalpopularity in majormedia polls since November2008 24 - 18 - number of polls 12 - 6 - 0 - 0% 25% SO% 75% 100% reported congressional approval rating 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 ro shrug a[, but many situations have multiple modes. or "typical" values. If you plot NBA Introduction 15 16 players by how often they were in the starung lineup In the 2012- 13 season. you get a bunch of au1letes clustered at either end. and almost no one in the middle: number of players 240- 180- 120- 60- 0NBA players by percent ofgames scarred, 2012-13 season --------•10')(, 2{)')(, J()')(, 4()'l(, 50')(, 6()')(, 70')(, 8()')(, 9()')(, 100% portion of games st~ That's the da1a telling us that coaches thmk a gtven player is either good enough to starr. or he Isn't, and the guy's In or out of the lineup accordingly. There's a dear binary system. Simtlarly. In our raongs da~a. men as a group mlght've seen women as "gorgeous" or "ugly" and left it at that: like top-hne basketball~alem, beauty could've been a you-have-It-or-you-don't kind of thing But the curve we Slarted with says somethmg else. Lookmg for unders~andmg In data Is often a matter of considering your resuJLS agatnst these kinds of counterfactuals. Sometimes, in the face of an infinity of alternatives. a srrmghtforward result Is all the more remarkable for being so. In fact. our graph 1s qu~te close to what's called asymmetric bera distribution-a curve often deployed to model basic unbiased decisions-which J'll overlay here: 16- 12- %of whole 8- 4- 0- 1.0 Dataclysm 2.0 perception offemale attractiveness real women, as rated by men - - unbiased curve 3.0 4.0 5.0 average received rating (on a 1· to 5-star scale) Our real-world da1a diverges only slighcly (6 percem) from uus formulmc Ideal. meaning this graph ofmale 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 predic~able. centered-maybe evenboring. So what? Well. thts ts a rare comext where boringncss ts something special: it implies that the lndtvldual men who dld the scoring arc likewise predictable. cemered. and. above all. unbiased. And when you consider the supermodcls. the porn. the cover gJrls, the Lara Croft-style femboLS. the Bud Ught ads. and, most devious of all. Ule Photoshop Jobs that surely these men sec every day. the fact that male opinion of female auractiveness is still where It's supposed to be is, by my lighLS. 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 cvcm. they're far more generous than the women. whose votes go hke this: 16- 12- %oi people 8 - 4 - 0 - 1.0 perception ofmale attractiveness vs. fomalo attractiveness 2.0 3.0 •men, as rated by women • women, as rated by men 4.0 S.O avorage received rating (on a 1· to 5-star scale) The red chan is centered barely a quarter of rhe way up u1e scale; only one guy in six is "above average" m an absolute sense. Sex appeal isn't somethmg commonly quamified ltkc this,so let me put it in a more famtl!ar 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 acruallyugly- ltested that by experiment. p!tlfng 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 dallng site l've seen: Tinder. Match.com. Introduction 17 18 OateHookup-sites that together cover about halfthesingle people in the United Scares. lt just turns om chat men and women perform a different sexual calculus. As Harper's put it perfeccly: 'Women are inclined to regret the sex they had. and men the sex they didn'C You can see exactly how it works In the data. 1will add: the men above must be absolutely full of regretS. Abeta curve plots what can be thought of as the outcome ofa 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 J'll equate with positive) just about as ofren as it comes up tails. Bur in our ~e see that the female one is weighted: it turns up heads only once every fourth flip. Alarge 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-coperson ratings co histOrical climate patterns. The male outlook here is very close to the function chat predicL<> cloud cover In NewYork City.The female psyche. by the same metric, dwells in a place sltghrly darker than Seatue. We11 follow this thread through the first of Daraclysm's three broad subjectS: the data ofpeople connecting. Sex appeal-how it changes and what creates itwill be our point of departure. Well sec why, technically. a woman is over the hill at twenty-one and the importance ofa prominent tattoo. but well soon move beyond connections of the flesh. Well see what tweetS can cell us about modem communication. and what friendships on Facebook can say about the stability of a rnarrtage. Profile piccures are both a boon and a curse on the Internet: they tum almost every service (Facebook. job sites. and. of course. daring) into a beauty contest. Well cake alookatwhathappens when OkCupid removes them for aday and justhopes for the best. Love isn't bhnd, though we find evidence it shouldbe. Part 2 then looks at the data ofdivision. Well 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 altitudes char most people would never cop to in public. and we11 see that racial bias Is not only strong but consistent-repeated almost verbatim (well. numeratirn). from site to site. Racism can be an interior u1ing too-just one man. his prejudice, and a keyboard. We'll see what Google Search has to say abom che country's most haced word-and what that word has to say about the country. Well move on co explore the divisiveness ofphysical beauty with a data set thousands oftimes more powerful than anything previously available. Ugliness has startling social costS that we are finally Da taclysm able to quantify. From there. we'll see what Twitter reveals about our impulse to anger. The service allows people co stay connected up to me minute: it can drive them apan ju~t as quickly. The collaborative rage chat It enables brings a new violence to char most anciem of human gatherings: the mob. Well see if it can provide a new understanding. as well. By the book's third section. we wtll have seen the data of two people imeracrtng. for better and for worse: here we \viii look at the individual alone. We11 explore how ethnic. sexual, and political identity is expressed. focusing on che words. images, and cultural markers people choose to represent themselves. Here are five of che phrases most typical ofa white woman: my blue eyes red hair and four wheeling country girl love ro be outSide Haiku by Carrie Underwood. or clara? You make d1e call! We11 explore people's public words. Well also see how people speak and ace in private, \vich an eye toward the places where labels and action diverge: bisexual men, for example. challenge our ideas of neat identity. Next. well draw on a wide range of sources-T\vitrer. Facebook. Reddit. even Craigslist-to see ourselves in our homes, both physically and otherwise.And we11 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 well see chat the Internet can be a vibrant. brutal. loving. forgiving. deceitful. sensual. angry place. And ofcourse ir is: ir's made ofhuman beings. However. bringing all this information together. 1became acutely aware that not everyone's life Is captured In the data. lf you don't have a computer or a smanphone. then you aren't here. 1can only acknowledge the problem, work around it, and wait for it to go away. 1 will say in the meantime that the reach of sires like Twitter and Facebo<;>k. and even my dating data. is surprisingly thorough. lf you don't use many of these services yourself. this is something you might noc appreciate. Some 87 percent of the United States is online, and that munber holds across virtually all demographic Introduction 19 20 boundaries. Urban to rural. rich tO poor. black to Asian to white to l..adno. a.ll are connected. Internet adopoon Is lower (around 00 percent) among the very old and the undereducated. wluch IS why Idrew my ·age line· well shon ofold age in these pages-at fifty-and why Idon't address education at all. ~1ore than 1 out of every 3 Amencans access Facebook ewryday. "The sue has 13 billion accounts worldwtde. G1ven that roughly a quaner of the world Is under age founeen. that means that somethmg hkc 25 percent of adultS on Eanh have a Facebook accounL Tile daung sites in Da~aclysm have registered some 55 million American members In the last three years-as I sald above. that's one account for every two Single ~ In the country. Twmer Is an especially Interesting demographic case. It's a glitzy tech success story. and the company Is almOSt slngle-handedly genmfying a large swath of San Francisco. But the service ItSelf Is fundamema.lly populist. boch in the "openness" ofItS plauonn and In who chooses to use IL For example. there's no sigmJicam difference In usc by gender. People wah only a high school education level tweet as mud1 as college graduates. ladnos usc dte service as much aswhites. and blacks \ISC 1t cwlce as much. And then. ofcourse. there's Google. lf87 percem ofAn1ericans usc the Internet. 87 percent of them have used Google. Titese big numbers don't prove I have the complete picrure ofanything. but they at least suggest that such a plctme Is coming. And in any event the perfect should not be the enemy of the bcuer-than-cver-before. The data set we11 work With encompasses thousands of times more people than a Gallup or Pew srudy; that goes wtthout saying. What's less obVlOUS IS that it's aaually much more Illelusive than most acaderruc behaVloral research. It's a known problem With exlsllng behaVloral sc.ience- chough a's seldom discussed publicly-that almost all of ItS foundational ideas were established on small batches of college kids. When I was a srudenr. Igot paid like S25 to inhale a slightly rad10acrivc marker gas for an hour at Mass General and then do some kind of mental rask while they took plcrures of my brain. It won't hun you. they said. lt's JUSt like spending a year In an airplane. they said. No big deaL they said. Whar they dldn'r say-and what I dldn'r realtzc chen-was that as I was lying there a little hungover in some lond of CAT-scanner thing. reading worclc; and clicking buuons with my foot. I was standing In for the rypical human male. My friend dtd the study, too. lie was a white college kid just like me. I'm wllltng 10 bet most of the subjecrs were. Tilal makes us far from rypical Dataclysm I understand how it happens: in person. getting a real represemar.ive data set Is often more difficult than the actual expenment you'd like to perfonn. 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 umvcrsity. But n's a big problem. especially when you're rcscarchmg bchcf and bcha\ior. It even has a name. It's called WEIRD research: white. educated. tndusllialized. rich. and democrauc. And most published social research papers are WEIRD.' Several of these problems plague my data. too. It wtll 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 mduscry are all too willing co encourage- ! feel compelled to d!sungulsh between the entrepreneurs and venrure capiralisrs you see on technology's pubhc stages. rnakmg sw1pmg gcsrurcs and spouting buzz talk 1mo headset mikes. people who are usually very WEIRD indeed. Erom the users of the scfVlces themselves. who are very much normaL They can't help but be. because usc of these services-Twitter. Facebook. Google. and the like- is d1e norm. As for the data's authemicity. much of It is, In a sense. face-checked because the Internet is now such a pan ofeveryday life. Take the data from OkCuptd. 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. chc true version. lf you upload a better-looking person's picrure as your own. or pretend to be mucll younger than you really are. you wtll probably get more dates. But imagine mceung those dates 111 person: they're expecnng what they saw online. If the real you 1sn'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 Imemet's worst fabulist Impulses in check. The people using these services. dating sites. social sites. and news aggregators alike. arc 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 • An anlc:lc In Slare notM: "WEIRD subj«ts. from countnes that reprtsent only about 12 ~rmu oC the world's populauon. differ from other popuhuons In moral decision nul:ing. reasoning ")'!c. fairness. ovtn things llkr VJSUal percepoon. Thls Is bcausc ~ lot of these bcluVIO~ and ~rcepoons m based on the cnviron~rus and co= in which "'~grew up· Introduction 21 22 archive: datalxlscs around the world now hold years ofyearning. opinion. and chaos. And because it's stored with crystalline precision it can be analyzed not only in the fullness of ume. bur wtth ascope and flexibility unimaginable just adecade 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 naggmg doubt. whtch. gtven my Luddite sympathies. pains me all the more:wnting a book about the Internet feels a lot like making a very ntce drawing about the movtes. Why bother? That's the question of mydark hours. 00 There's thts great documentary about Bob Dylan called Oonr Look Back that I watched a bunch back In college; my best friend. Justin.was srudying film. Somewhere In the movie, at an after-pany. Bob gees imo an argumem with a random guy about who did or who dtd not throw some glass thing in the street. 11tey're both clearly drunk. 111c 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 justltke 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 btgger noise than you. man. GUY AT PARTY: I'm a small noise. DYLAN: Right. And then someone breaks tt 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. manyrs. saviors. even scoundrels (especially scoundrels!)-thctr lives are how we've wld 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- thc heroic framework Is how people order the world. Narmer was first on an anciem list of ktngs. The scribes have changed. but that list has continued on. I mean. the 1960s. power to the Dataclysm people and so on. is the perfect example: that's the era of Lennon and McCan·ney. Dylan, Hendrix, not "Guy at Pany: Above all. Everyman's existence hasn't been wonh recording. apart from where it intersectS with a legend's. Bm this asymmetry is ending; the small noise, the crackle and htss ofthe 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 Ihope. evenrually democratize our fundamental narraove. The sound is inchoate now. umefined. But I'm writmg this book tO bring out what faint patterns I, and others. detecl This Is the echo of the approachmg tram 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 \Vfiring tO. I know there are a lm of people making big claims about data. and I'm not here to say it will change the course of history-cenatnly not ltke Internal combustion did. or steel-but it will. I believe. change what histOry Is. With data. histOry can become deeper. 1t 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 \'lith his friends and family and live life msmall ways. this means something to me. Now. as much as I'd hke me and you and WhoBeefed8Ito be right there on the page ,...;th the prestdent when future works creat this decade. I Imagine everyday people ,...;}1 always be more or less nameless. as mdeed they are even here. The best data can't change thal 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 rrtamage both drove and reflected broader acceptance of homosexuality or how village society tn Asia was uprooted. then created again. within its large urban centers-Inside that story. even comprising its very bones, ,...;}1 be data from Pacebook.Twitter, Rcddtt,and the like. And if not, our putative writer will have failed. I've tried lO caprure all this with my mash-up title. Kataklysmos is Greek for the Old Testament Flood: that's how the word "cataclysm" came to English. Introduction 23 The allusion has dual resonance: there is. of course. the dam as unprecedemed deluge. What's beingcollected wday 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 ofa world transformed-ofboth yesterday's stumed understanding and today's limited vision gone with the Aood. Thisbook is a series ofvignettes, tinywindows looking inon our lives-what brings us together, what pulls us apart, what makes us who we are. As the data keeps corning, the windows will get bigger, bur there's plenty to see righr now, and the first glimpse is always the most thrilling. So lO the sills, 111 bodsryou up. 24 Dataclysm ·z. Death by a Thousand Mehs In 2002, the Oscars hired the director Errol Morris to shoot a short ftlm about why we love the movies. The Academy wanted to kJck 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 cue of che 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, lggy Pop. AI Sharpton. Mikhail Gorbachev. Trump and Gorbachev were back to back. and somewhere out there there's a picture of che cwo of them. with me in che middle. phocobombtng before phmobomblng was a thing. Isay "somewhere" because right afrer the flash. Trump snapped his .fingers. and his bodyguard wok Jusctn's camera. For hts favorite movie,Trump ptcked Ktng Kong. because he of course likes apes who cry tO "conquer New York." Gorbachev. through a translator whose mustache must've weighed cen pounds. chose Gladlaror. At 2:01 in Morns's film. the wide e)'CS and the voice saying "The Omen" arc mine. Now, I like a good Antichrist moVJe more than most people. but I chose The Omen more or less at random. There arc so many good movies. I'm actUally not sure what my favorite one is. But I know my least favorite film with absolute certainty. Peeker. by John Waters. Iwalked out of iL Twice. Iwent once with some friends. couldn't deal with the mondo-trasho vtbe, not lO mention the exaggerated accents. and just had w leave. 111e next weekend. some orher friends were going and since John Waters IS a respecced ameur. and hey I'm a cool guy who gees ll. I figured mere was at least some chance I was wrong the first llme. Also I had nothmg else w do. So I went agaJn. Such is the temporary madness of bemg rwemy-rwo. 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 rejectlon- u's practlcally his calling card as a d1reccor. Let me put it thiS way: nobody leaves Peeker thmkmg it was "meh": Death by a Thousand Mchs 45 46 either you loved it. or goc the hell om after twency mlnmes like I did. C\'licc. That's by design.' 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: /rljohnWarers.t and whtle it's net the most-trafficked URL ever. people actually put scuff there: news. old clips. questions abouc him, comments, and so on. There's a/r/GeorgeLucas. too: it has one post. ever. Ifyou enter /r/SrevenSptelberg Into your address bar. you get "there doesn't seem to b~thlng here" from Reddtt's server because. as good as hts 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 morivauon to, say. make a fan sne. and that motivation is often Intensified by feeling like you're pan of a special. embattled elect. Devotion ls 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 Ibring him up net only because of my lifelong personal struggle with Peeker. but because Waters also gecs the: universality of d1e principle: it's not just rrue for an. He's gor a lot of great quotes, but here's one iliac speaks right to me: "Beauty is looks you can never forget. Aface should jolt. not soothe." He's completely correct. for as with music. as with movies. and as wtrh a wide variety ofhuman 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 errhanced when some men find her ugly. You can see thiS In the profile ratings on OkCupid. Because the site's mtng system Is 5 stars. the votes have more depth than just a yes or a no. People give degrees of op1mon. and that g1ves us room to explore. To show this finding, we'll have to go on a short mathematical journey. These kinds of exercises arc what make data science work. To put wgether puzzles. you have to lay out all the •Waterson film·"To ~ b.td =e Is what entcttalnmcm IS all abo111. If someone \'Omits while watchtng one of my ftlms. tts hl:e gemng • snnd!ng ovaclon." t These pagei on Reddlt are called subreddtlS. 111 cxpbin the she and tiS nu>ncesIn more detnlllater. Oataclysm ~ieces and then just start rrying things. In the absence of careful sifting, rcducoon. and parsimony. very little just "jumps ourat you· from terabytes ofraw data. Consider a group ofwomen with approximately the same am-acnvcness. let's jusr say the ones rated in the middle: 16 - • women, as rated by men 12 - %of whole 8 - 4- 0- 1.0 2.0 3.0 4.0 s.o average received rating (on a 1- to S·star scale) l ow i.magfne a woman fn that group and think of the many differem votes men couldve given her-bastcaJly think about how she ended up 111 the middle. There arc thousands of posslbthties; lu:n: are JUSt a few 1made up. combinations of ls. 2s. 3s. 4s. and 5s. which all come to an average of 3: number o f men who voted.•. .,. ·2· "3" "4• pattern A 100 •5• panern ------------------------------~-- ·~ 3.0 pattern B 10 80 10 3.0 - ....... . - ·pattern C 10 20 40 20 10 3.0 pattern 0 25 25 25 25 3.0 pattern E so so 3.0 As you mfght've noticed. the vote pauems I've chosen get more polarIzed as they go from Pattern A to Pattern E Each row sull averages om to ilia I •• b t same cemra 3. ut they express that average In differenr ways. Panern Ais the Death by a Thousand Mehs 47 48 embodiment of consensus. l11ere. the men who cast the votes have spoken In perfect uruson: this woman is vcacrly In the middle. But by the orne we get tO the bOllOm of the table. the overall average is still centered. yet no single individual acrually holds that cenrral op1nion. Panem E shows the moSt extreme possible path to a middlingaverage: for every man awarding our theoretical woman a "1." someone else gwes her a·s:and the total resultcomes out to a·3· almost in sp11e of itself. That's the John Wacers way. These pauems exemplify a mathematical concept called varia.nce. It's a mea"sure of how widelydata is scanered around a central value. Variance goes up ffie' further the data points fall from the average: m the table above. it Is highest In Pattern E. One of the most common applications ofvanancc Is to weigh volatility (and therefore risk) in financial markets. Consider these two companies: Assoc~ted Widgets S 110 Widgets Inc. $110 Sl SIOO _ - - ·-===-==-- -- --- -- JF M AM J J A S ON O J FM AMJJASONO Both rcmrned 10 percent for the year, bm they arc very different Investments. Associated Widgets experienced large swings in value throughout the year, while Widgets Inc. grew linle by little. showing consistent g.uns each month. Computing the variance allows analysts to capture tins dlstlnclion In one simple number. and all other things being equal. mvesrors much prefer the low score of that pauem on the right Same return. fewer heart palpitations. Of course. when it comes to romance. heart palpitations are the rerum. and that gets tO the crux of It It rums out that variance has almost as much ro do with the sexual aucnlion a woman gets as her overall attractiveness. In any group of women who are all equally good-looking. the number of messages tllcy get Is highlycorrelated to the variance: from tile pageant queens to tile most homely women to tile people nght In between. tile md!V1duals who get the most affection will be tile polarizing ones. And the effect isn't smallbeing highly polanzmg willtn fact get you abour 70 percent more messages. That means variance allows you 10 effectively jump several "leagues· up in the dating Data clysm pecking order-for example. a very low-rated woman (20th percentile) witll high variance m her votes gets hit on abour as much as a typical woman in the 70th percentile. Pan of that Is because variance means. by definloon. mat more people like you a lor (as well as dislike you a lot). And those enthusiastic guys-lee's just call them tile fanboys-are the ones who do most of the messaging. So by pushmg people toward the high end (the Ss). you get more action. But the negarive votes themselves are p:m of tile story. too. They drive some ofthe auenlionon tl1e1r own. For example. the real pauerns cxemp!Jfied by Cand D below get about 10 percent more messages than the ones shO\Vll m Aand B. even though tile LOp cwo women arc rated far better overall: number of men who voted ... "1" "2" "3" "4" ·s· panern avg. woman A 2 22 27 29 20 3.4 woman B 10 13 31 28 18 3.3 woman C 32 22 i2 16 18 2.7 womanO 47 13 6 19 IS 24 I've been talking about messages as if they're an end unto themselves. but on a datingsite, messages are the precursor to outcomes like in-depth conversations. the exchange of contact information. and evemually tn-person mccungs. People with higher variance gee more of all these thtngs. mo.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 m terms of her absolute raung she's much less attractive. Moreover. tile men giving out those ls and 2s are not themselves hnttng on the women-people practically never contact someone tlley've rated poorly: It's that haVIng haters somehow mduces everyone else to wam you more. People nor • Only 02 ptrccm of the m~ on the sue a~ ~r.t 1>)·1~~ to • ptrson to "'hom they .....-ardod f.,.,.-e1 than 3 sta~. Death by a Thousand Mehs 49 so liking you somehow brings you more anemion ennrely on its own. And. yes. in his underground castle, Karl Rove smiles knowingly. peuing an enormous toad. It only adds to the mystery of the phenomenon that OkCupid doesn't publish raw amactiveness 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 gotng on. Suppose a guy is amacted to a woman he knows Is unconvcnuonal-looking. Her very unconventionality impltes that some other men are likely rurned off: It means less competition. Having fewer--riVals increases h1s chances of success. I can 1magme our man browsmg her profile. circling his cursor. thinking to himself: Ibet she doesn't meet many guys who rh!nk she's awesome. In fact. I'm actually Into herfor herquirks, not in spite ofthem. Tins is my dtamond in the rough. and so on. To some degree. her very unpopuiMlty is what makes her artracrive to him. And Ifour 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-v:mance side-a relatively anractive woman with consistent scores is someone any guy would consider conventionally pretty. And she therefore m1ght seem co be more popular than she really iS. Broad appeal gtvcs 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. TitJs ISmy theory at least. Bm the tdea that variance is a positive thing Is fatrly well established in other arenas. Social psychologists call It the "pratfall effect"-as long as you're generally competent. makmg a small. occasional mtst:ake makes people think }'Ou.re more competent. Flaws call out the good sruff all the more. Thts need for imperfecnon m1ght JUSt be how our brains are pm together. Our sense of smell. which is the most connected to the brain's emotional cemer. prefers dtscord to unison. Sc1emists have shown this in labs. by mixing foul odors with pleasant ones. but namre. in the wisdom of evolunonary ume. realtzed it long before. The pleasant seem gwen off by many nowers. 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 nowers don't smell as good without 1t. Alittle bit ofshit brings the bees. Indole ts also an ingredient In synthetic human perfumes. Oataclysm You can see a public lmplememation. as it were. of the OkCuptd data tn the rarefied world of modeling. The women are all profesSIOnally gorgeous-5 stars om of 5, of course. 13m even at that high level It's still about distinguishing yourself through tmperfecuon. Cindy Crawford's career took off after she stopped covering her mole. Linda Evangelista had the severe hair-you can't say a made her prerrler, but It dtd make her far more lmcresting. 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 arc more relatable than swimsuit models. will help you see how It works for a normal person. Here are stx women. all with middle-of-the-road overall scores. but who tend to get extreme reactions either way: lots of Yes. lms of No. but very liule Meh: TI1anks 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: \vtth body an. a snarkyexpression. or by c;lLing a grilled cheese like a badass. And you find many relatively normal women with an unusual rrau: ltkc the center woman tn the bottOm row. whose blue hair you can't see in black and white. And you Death by a Thousand Mehs 51 espec1ally see women who've chosen to play up the1r particular assec/llabiliry. If you can pull ofT. say. a3.3 ratingdespite the extra pounds or the people who haec tattoos or whatever. then. literally. more power to you_ So at the end of it. given that everyone on Eanh has some kind of flaw, the real moral here Is: be yourselfand be brave abour IL Certainly rrying to fit m, just for irs own sake. Is counterproductive. Iknow this is dangerously close co the kind of thing that gets put on a qUilt. and quilts. being the PowerPoim presemactons ofan earlier time.are the opposite ofscience. It also sounds alor like the advice a...._ mothergives. 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 1s. in the numbers. Uke I said. people can feel the math behind things. espedally. thankfully. moms. I juSt \viSh she'd cold me that by ninth grade bears aren't cool. 52 Dataclysm \· ... 5. There's No Success · Like Fai ure There's a great Tumblr called MClients from Hell,"where anyone can submit their se!VIce-lnduscry horror Stones. 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: Ifthe reader misses the caption. then they v.rtll still see the heading. DESIGNER: It would be qutte unusual to have both a heading and a caption on a photo. CLIENT: That makes sense. Just pm a heading next to thecaption. then. My favorite client quote on the site right now Is: "l don't like the dinosaur in lhis graphic. ll looks too fake. Usc a real pholO of a dinosaur inStead." Tite blog mosdy getssubmisstons from graphtc destgners. but Cltents from Hell"s populartty speaks lO a universal truth. People hate their customers. I don't mean hate on an individual level bm. en masse. customers. like any rabble. are lO 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 ahvays right" thtng-nobody likes a person with that much power. But by far the biggest cause of frustration is dtat people don'l understand and can't aruculate what they actually need. As Steve Jobs said. "People don't know what they want uno! you show It to them.- What he didn't say is that showing them. especially in tech. means playing a game of Pm the Tail on the Donkey wtdt several million people shouting advice. Ifyou are, say. acar companyand people don't like some part ofyour product. they mOStly lell you indirectly. by not buying tt. 11tere's historically been no open channel ~-een Ford and the folks who want the cup holders m be green or who think It would be better if the Steering wheel were a square. because. you know. most turns are 90 degrees. 11m's why traditional companies spend so much on market research- they have lO Stay 'vay ahead of these kinds of things. because by the time acompany like Ford would naturallyhear about a problem. via /\ccoums Receivable. tt's 'vay loo late. 1\ website Is different: if people have acockamamie idea. someone al the comThoro's No Success Like Failure 85 86 pany Is just an e-mall away. And tipeople don't use something. the Site nodccs Immediately. MeasurementS are cracked In real time,down ro the finest gram. everywhere. Wheneveryousee something newonyourfavortte site-Google. Facebook. Unkedln. YouTube. or anywhere-and you click it. know that someone. probably wcanng headphones and eating Doritos. JUSt saw a !Jule counter go up by 1. That's when the richness ofdata can drtvc a person crazy: one ofGoogle's best destgners, the person who in faa bullt their visual design team. Douglas Bowman. evemually quit because the process had become too microscopic. For one bunon. the company couldn't decide be{ween twoshades ofblue. so they launched all forty-one shades in between tO,_ seewhich performedbetter. Know thyself: It was etched imoafOOtStOne ofthe Temple ofApollo at Delphi But ltke the rest of the best wisdom that time has to offer. Itgoes right om the window as soon as anyone rums on acomputer. 1ot knowing what customers need from a car. or even &om a parucular webSite interface-those are mauers for a business school or a design workshop. It's when people don't understand their own heans that 1get Interested. People saying one thing and doing another is pretty much par for the course In social science. bur I had a rare opportunity to see people acting In £\vo comradlclOry ways. And It all happened because I didn't know what they wanted either. 00 OnJanuary 15. 2013. OkCup!d declared "Love ls Blind Day" and removed everyone's profile photos from the site for a few hours. The idea was tO do somedung different and get a httle auennon for a new seMce we were launching at the same time. The programmers ·nipped the switch"at nine a.m.: new conversations started per nour Oataclysm SOle • .eok - 30k - 20k • 10( - 0 - January 15.2013 ------· a normal Tuesday 0:00 3.00 6:00 9:00 12:00 15:00 18:00 21 :00 time of day It was a bona fide pit ofdespair-rare in the wild! The newserv1ce OkCupid was trying to promote was a mobile app called Crazy Blind Date. Wtth a couple taps on the screen. it would pair you w1th a person and select a place nearby and a time in the near future for the two of you to meet ·n1e app provided an Interface to let both parries confirm. bm 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 che best. You've probably already nouced that I'm speakmg of Crazy Blind Dace In the past tense. Even after a quaner million downloads. 1t failed. because In che end people Insist on seeing what they're getting Into. The app was one of those ideas that looks great on a whlteboard and miserable In chc full color of creation-It was l!kc one long "Love Is 13l!nd Day." and with a CBD-style scramble ofa stockphoto no way to n.p the swnch 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 m the sky (litdc-known fact: there arc no bugs 111 heaven, JUSt sweet features). about 10.000 people used it co share a beer or a cup of coffee wnh someone they'd never seen or spoken to before. From these Intrepid few. the app bequeathed the world a rare data sec. Crazy Bl!nd Date recorded not only the fact that dater A and dater B mcc 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 OkCupld accoums. we were able to cross-reference this data with all kmds ofdemographic derails. We suddenly had In-person records to combine with our massive collection of digital Interactions. When you merge the cwo sources you find something remarkable: the two people's looks had almost There's No Success Like Failure 87 88 no effect on whether they had a good time. No maner which person was better-looking or by how much-even in cases where one blind-dater was a knockout and the ocher rather homely-the percem of people giving the dates a positive rating was constant. Auracciveness didn't maner. This data, from real dates. turned everything l'd seen in ten years of runnmg adating site on its bead. Here are the numbers for men. I've expressed amaclivencss below as the relative difference in a couple's mdividual raungs. rather than as absolutes. I did this to capture the fact that a person's happmess 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 dlfferemly. I've included the coums of dates as che bars co show that che balance In attractiveness berween 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 slywhen their blind date arrived and didn't pass muster. The satisfaction numbers (for males) arc the percentages in red: how arrracriveness affects male dare satisfaction ISO - 120. 90- 60· 30 o- L-~--~~--~~--~~~-L--L-~--~~~ woman much hotter eo.en man much hotter attraaivencss disparity And following is the same data for women: Dataclysm how arrracriveness affects female dare sarisf:perience. the red from Crazy Blind Date. In shon. people appear to be heavily preselectingonline forsomething that,once they sit dO\'ffi In person. doesn't seem imporrant to them. That kind of superficial preselection Is everywhere. In fact, there's a IOl 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 medlcme like a ktng snake and plan to just swallow the package whole, there's really no reason to pay cw1ce as much for the ·name" molecules. whose properties arc detcrmmed by immutable chemical law. And yec 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 compatiblhty. self-described Democrats and Repubhcans 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 quesuon you answer. and you can pinpoint the answers that you would (and would not) accept from a potenual match. Despite all this control. m 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 gomg on actual dates). it's caring about politics. one way or the Olher. that Is actu· ally more important to murual compaubtlity than the derails of any particular belief. We confirmed this ma summer-long experiment in 2011 People tend to runwildMth those match questions. markingall kinds ofstuff as "mandatory." in essence putting a chcckhst to the \,rorld: I'm looking for adogloving. agnostic. nonsmoking libcrnl who's never had kids- and who's good m bed. of course. But very humble qucsuons like Do you like scary movies? and Have you ever traveledalone to another country?have amazing predictive power. If you're ever smmped on what tO ask someone on a first dare, try those. In about three-quaners 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 ro overemphasize the big. splashy things: faith, politics. and cenamly Dataclysm looks. but they don't matter nearly as much as everyone thinks. Someumes r.iey don't matter at all. Fiasco though It was. Love Is Blmd Day gave us a VIsceral example of what people do In the absence of informadon. In htding pictures but chang1ng nOthing else. we created a real-lime expenmcm to set against the sire'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 ofthe upshor \vas predmable. People scm messages \VIthom the ryptcal biases. or racial and amacoveness skews. What a user couldn't sec. he couldn't 1udge. But of the 30.333 messages sent blmdly. eventually 8.912 got rcphes. a rate about 40 percent hlgher than usual. And m the dark. for those who were there. something astounding happened Twemy-four percent of rhe 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 ex-pected number in that amount oftime is barely halfthat.So not only were people writing messages that were far more likely to get replies. they were gJving out phone numbers and e-mail addresses at a h1gher rate-to people they'd never even seen. For the couples who began Cllking and were still gemng 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. m the data. you can acrually see them spook. Threads straddling the momem we fhppcd the swnch lasted an average of 4.4 more messages. When you compare them agamSt a control data set. they should've lasted 5.6. Evenrual comact-mfo exchanges In those "hghts on" threads were dO\'ffi by as1m1lar amount. Daung Sites are designed to wve people the tools and the mformauon to get whatever they wam om of being smgle-casual sex. a few fun dates. a panner. a marriage ...anything Stuff l!ke height, polincal 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 pickapan. there's aside of it that I think does love adisservice. People make choices from the mformation we proVIde because they can. not because they necessanly should There's No Success Loke Failure 91 I can't help think of the many people getting turned down because of some perceived "deal-breaker· that acrually noone cares about and wonder Ifthe Internet has changed romance in the way ic's changed so much else- and for 1.he same reason. If I may channel my inner ancl-Jagger: Online. you can always get what you wanL But what you need. that's a much harder thing to find. 92 Dataclysm 7. The Beauty Myth in Apotheosis _/ I workin 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 darks: to say nothing ofblack or white or Asian orgay or straight. or neither, ~r 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 bm on the other is as inborn as a person's race or sexuality. and like those laner rraits It's often resisr.am 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 anyheir, 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 plmred new messages received per week. by the recipient's physical amacctveness: 16 - 12 messages/ a _ week 4- 0- . . .- ·Oth 1Oth 20th 30th 40th 50th 60th 70th 80th 90th attractiveness percentile The sharp rise out at the right smashes down the rest of the curve. so 1ts true nature is a bit obscured. but from the lowest percentile up. this is roughly an The Beauty Myth in Apotheosis 11 7 118 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 effea. there Is htcle notlccable difference belWeen. say. a l.O and 2.0-these cause rremors that vary only In degree of lmpercepdbility. Bur at the high end. a small difference has cataclysmic Impact A 9.0 Is lmen.se. bur a 10.0 can ruprure the world. Or launch a thous.1nd ships. What you definitely can't see In the chan 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. messages/ week 30- 25- 20- IS- 10- I . .../ 5- .............. ........................................,~.;.·"·"•''•"'·'•"''•""'"···· _,) o~ IOlh 20th 30th 40th SO!h 60th 70th 80th 90th attractiveness pcrcontile all men It's hard for me to convey how much auemlon the upper-rtght corner of thiS curve entails. shon of tracking you down and screammg In your face abour my hobbies. Especially mlarger mles. where the message flow Is 50 perccm higher than even what you see above. a woman at me lOp of me scale has somethmg like a term paper's worm of hey-what·s-up-do-you-llke-motorcycles-bccause-1ltke-mOiorcycles wamng for her every ume she comes to me site. A dudcclysm. 1f Dataclysm _/ you will. However. nelmer beauty's effects. nor me male/female split. arc confined to the sexual realm. Here Is data for Interview requests on Shiftgtg. a Job-search site for hourly and service workers:· 6- snumber of 4 - interview requests 3- received 2- 0Oth 1D-.h 20th 30th 40th 50th 60th 70th 80th 90th attractiveness percentile And for friend counts on Facebook: number of Facebook friends 700- 600- 500- 400- 300- 200- 100- 0Olh IOlh 20th 30th 40th 50th 60th 7()-.h 80th 90th attractiveness percentile ·..orr£· men W0f1"1 men • l foreground trend lines here because the Wl.l ls shghuy sp3rser and thrrefore mort notSy than uswJ llus sarnpl~ :s ..s.OOJprop!< The Beauty Myth in Apotheosis 119 120 Success and beau[)' are correlated for both sexes. bm 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. lt 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. ln 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 Ok- _/ Cupid, even though they're applying for a job. Male HR reps weigh the female applicams' beau[)' as they would in a romantic setting- which is either depressing or very. very exciting. depending on whether you're a lav..yer with a litigation practice. And female employers view it through the same (seemingly sexualized) lens. despne there ([)'Pically) being no romantic inrem. It is hardly fresh lntellecrual ground that beauty matters. and chat it matters more for women. For example, a foundational paper of social psychology is called "What Is Beautiful Is Good." lt was the fJrSt in a now long line ofresearch to establish that good-looking people are seen as more imelligent, more competent. and more trusrwonhy than the rest of us. More attractive people get better jobs. They arc also acquitted more often In court, and. failing that, they get lighter sentences.As Roben Sapolsky notes In the Wall Street Journal, two Duke neuropsychologists are working on why: "The medial orbitOfronral conex of the brain Is Involved in raring 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. ln other words. the brain ... assumes that cheekbones tell you something about minds and hearrs:· On a neurological level, the brain registers that ping of sexual attraction- Ooh, she's hot-and everything else seems w be splash damage. To my second point. dm beauty affectS women in particular. Naomi Wolfs bestseller TI1e Beauty Myth showed that better than 1ever could. ln short, my raw findings here are not new. What Is new is our ab!li[)' to test ideas. established ones. famous ones even. against the atomized actions of millions.That granularicy gives strength and nuance w previous work, and even suggestS ways tO build on it. The paper "What Is Beautiful" was based on a research sample of only 60 Dataclysm subjecrs-barely adequate to prove the effect. let alone ItS many facets." But now we can go from "What Is Beautiful Is Good· to asking "How Good?" and In what contexts. 1n sex, beauty is very good ln friendship. it's only somewhat good. and when you're looking for a job. the effect really depends on your gender. As for Wolfs seminal work we can confirm the truth behind her broad observation that "today's woman has become her 'beamy"'- three robust research setS agree that the correlation is strong. And.better. we can extend some of her most cogent arguments abom beaury being a means ofsocial control. Think about howthe Shiftgig data changes our undefS[anding 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 abili[)' tO do ajob well. Meanwhile. men have no such selection imposed. It is therefore simple probability that women's failure rate, as awhole. 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 ro (statistically) guarantee poor performance. It's either chat or you limit their opporrunitles. Thus Ms. Wolf: "The beauty myth is always actually prescribing behavior and nor 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. 1have a young daughter. and in our rare downtime, Reshma and l 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 dnmks in a bar will always argue. Every family must have their mvn particular flighcs of fancy. but ours go more or less like most, I imagine. My wife or I will srarr. it doesn't really matter who: Our liccle girl's going to be so smart. Oh yes. we'll teach her everything we can. She'll be so gende, so good-hearted. These • The scudy of beauty by traditional methods Is cspcctaUy susceptible to !he problem of msufficiency. If your researchtopic Is.say. "'~alth. you can '~ry easily get a measure of so=one's net wonh or Income and then move on tOthe dependent trait you want tO look aL But tOstudy beauty. first you have tO determine howgood-looking your subjectS are. which iS a resourcc-mtcnslve process. Beauty beingso wildly subjecove (as opposed to, say. harr color. where If you crowd.sourced It, you m1ght get slight vartltlons-broWFI. bruneue, ch-e seen v.1th WEIRDness earlier, that has nOt lxen a strength ofpast academic research. The Beauty Myth in Apotheosis 121 122 thingsare very Important to agood Life. we agree. And ofcourse. look ac that skin. like chal. chose eyes. shell 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 rum. But nor coo pretty. nght? Yeah. we wouldn't wam that We both sit back. and the conversauon moves on to somethmg else. This is what tt comes down to: I can't Imagine anyone wishmg limits on a son. Unfommately. it's a problem the internet is surely making ""'Orsc: for Tht Beaury Myth social media signals Judgment Day. Your ptcrure is attached to pracncally everything. certainly every r~umt!. every appllcadon. every byline. If people care about what you are doing. they will find our what y'OU look hke. Not because they should. but because they can-Faccbook and Llnkedln have essentially extended OkCuprd's love Is Blind problem to everythmg. Even JUSt ten years ago. rt was almost rmpossrblc co de the average person's name to her photograph: now you jUSt Coogle the words-everyone does-and up pops a thumbnail from a social network. We've all had to prck through snapshots for that 'best- one. Choose wisely. friends. because it defines you in a way it never has before. There's a momenrum co the rrendthat might noc be obvious to people who work outside the industry. l11e new design standard of the last two or three years, more open and more phococemric-what l think of as "Pimeresty"-ts making not just pictures. but beawyspec~cally 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: 1-------."··'; Dataclysm ··"..~·,;~·"' ,....······•···· ....······/ .. The designers just wanrcd the page to look more modern. What they didn't anticipate (and later had to mitigate) was t:he following: all those extra pixels allowed the pretty faces co outshine the others all the more. The rich got richer. It was tl1e web-destgn equivalent ofAmerican domestic policy. change in incoming message volume +80% Chh 1Oth 20th 3Chh 40th 50th 60th 70dt 80th 90th attractiveness percentile Given th1s pressure it's no wonder that body-image blogs arc so prevalent. And that posts tagged like #thinspiratlon 1/thinspo 1/loseweighc //kceplostng #proana 1/thighgap became so common that bmh Tumblr and Pmterest (mdependem of each ocher) had tO alter theirTerms ofService to ban thrs kind ofcontent Ifyou're wondermg what the last two hashtags are. #proana isshort for "pro anorexia--people tn favor ofstarvauon as a wcighr-loss technique. Meanwh1le. #thighgap refers to having thighs so thin that they do not touch when you stand w1th your feet and knees together. It's a trait fetishized by teenage g1rls. Qune apart from the quesuonable deSirability. u's biologically cmposs1hle for most of them. The full depravuy of the phenomenon can't hit you umil you search for these tags yourself and are confronted w1rh an unendmg page ofbroken bodres tilting at the camera- nOt only are the "mspirlng" women deathly thin, they are also frequently in hngerie. bikims. underwear. The blogs. created by'~'Omen. are truly the epitome of the male gaze- and I say chis as a person reflexively skeptical of the language of the academic lefr. Tumblr and Pmterest banning the contentdidn't solve anytl1ing. ofcourse. least ofall cl1eir users' body-image issues. so the sites arc now raktng anotl1er approach. The Beauty Myth in Apotheosis 123 Because these blogs are tagged. they are able to imervene algorimmJcally-search for mighgap on Tumblr and me screen goes blank, an overlay appeanng: "Ifyou or someone you know is dealing with an eatlng disorder .. : A link to help and resources follows. It is asmall measure,buc before the behavior was dlg!uzed. mere was practically no way tO get directly at this problem. at least not until visible damage had already occurred.111ere was onlyrumor-an ear at the bathroom door. perhaps a parent's sad suspicion. Dara is about how we'rJ really feeling- feeling about one anomer. yes. but also abour ourselves. If it finds divides in our culrure. our politics. our habitS. our mbes. it finds divides Wlmin liS, !00. And dm's ahopeful thought. because for anything (0 be made whole. me first step is w know what's missing. 124 Dataclysm 8. It's What's Inside That Counts A Note o n the Data Numbers are mcky Even Without comext. they give the appearance of face and their spec~ficity forb1ds argum~m· 20.679 Physicians say 'LUCK1ES nr<' lc~ lrntclling... Whacelse 1s there to know ~hour smoking. nghr:> The illu~Jun is en:n stronger when the numbers are dressed up as srausuc:; I won'r rehash the old wisdom rhere. Bw behmd every number chere's a person making decisions: wh:n to :malyze. what to exclude. what fr<~me 10 sec around wh:uever picrure:-. the numbers p:-tmt. To make ast:Hemem. even to just make a Mmple graph. IS to male choices. and in rhose cho1ces human imperfection inevllably comes through. As far :ts I know. I've made no 111011V:ned dectslon that has bem the outwme of my work- the dara of people acung out their lives is imere~ting enough withom me needing 10 lead irone wayor anothr:r. Bw Ihave made choices.and those choke:. have alfected che book. I'd like tO walk )'Oll through a few of them. My fir..t choice was probablr Ill}' most difficult. the dcct>ton ro focus on male-female relacionshtp::. when I talk about auracuon :~nd sex. Sp:~ce. ofcuur.;e. w;~~ <1 facwr- to include Snmtnt. You May lk • Crlmin>l." horh pubi!Mled by rhe EIswne adoption among Asian Americans is similar. Adoption is above 80 pacem tor all age groups. save people sixty-five and older. Susannah Fox.and Lee Rainie. "lmernct User~ in 201{' Pew Research Internet Project, Pew Research Ccmer, February 27. 2014. pcwimerncc.org/ files/2014/02/12-imernet-user:.-in-20l'I.Jpg. Notes 25 1 20 More than 1our of every 3 Americans access Facebook Facebook reponed 128 m!Uton US users in August 2013. Facebook had at least 1.26 billion users worldwide m September 2013. World and US population statistics are from Wtkiped~a. See expandedrambltngs.com/index.php/by-the-numbers -17-amaz.ing-facebook-stats/. 20 fundamentally populist Tius tS something like common knowledge among people who srudy social media adoption beyond the Google Glasshole/ Technocrat use case. Sec Pew Research Center's -Demographics of Key Social Networking Platfomls· (2013). The repon sh0\1/S no statistically significant difference in rates of Twitter usc between the "high school grad or less" and "College +• educanonal cohons (coming in at 17 percent and 18 percent, respecuvely). Pew surveys a random cross-secrion of Americans eighteen years old or older. so very few of the "high school grad or less" cohort are chat 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: pewimernet.org/2013/12/30/demographics-of-key -·!;ocml networking-platforms/. 21 Jr's called WEIRD research This fact and my general take on the phenomenon are adapted from "Psychology Is WEIRD," by Bethany Brookshire, in Slare. Sec also !he Roar of the Crowd." The Economist. May 24. 2012. economist.com/node/21555876. 22 Pharaoh Narmu As you can imagine. this is up for debate. though Narmer. also known as Serket. is a defensible choice. In earlier drafts I had Gtlgamesh. the Akkadian hero. mthis place becauseJ. M. Roberts. in his Hislory ofrite World (New York: Oxford University Press, 1993).chooses Gilgamesh. I eventually went wtth Narmer because his life is dated several centuries earlier. and he seemed to me as likelytO have acrually Lived. Yahoo! Answers also mentions Elvls Presley. Chapter 1· Wooderson's Law 34 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 252 Notes needed to have received votes from at least rwemy-ftve other people. For something as idiosyncratic as attraction. I felt an average score comprising fewer than twenry-five votes wasn't rehablc. 39 per the US Census These numbers are from the US Census Bureau's "Mantal Starus ofPeople 15Yearsand Over. byAge. Sex, Personal Earnings. Race. and Hispanic Origin. 2011." Chapter 2: Death by a Thousand Mehs 46 ·Beauty is looks you can neverforget" John Waters. Shock Value:A Ta.stefill Book About Bad Tasre (Philadelphia: Running Press. 2005). p. 128. 48 concept called variance I used standard deviation to measure vanance throughout this chapter. 50 the "pratfall effect" A Google search for "pratfall effect" will yield many examples. I particularly relied on the pr~cis "The Positive Effect of Negative Information" by Bill Snyder and rhe 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 ofConsumer Research 38. no. 5 (2012): 846-59. 50 Our sense ofsmell For this passage. I relied on Fabmn Grabenhorst et al.. "How Pleasam and Unpleasam Stimuli Combine in Diffcrem Brain Regions: Odor Mixtures."Journal ofNeuroscience 27. no. 49 (2rol): 13532- 40. doi: l0.1523/JNEUROSCI.3337-07.2007. Wikipedta's "Indole" enrry describes its "intense fecal smell· For more on indole's role In perfumes and in naruraUy occurring flower scents, see, as I dtd. perfumeshrine.blogspot com/201%5/jasmine-indolic-vs-non-indoltc.html. 51 Here are six women We received these permissions using a double-blind system. to protect user privacy. I submtued criteria (women, high variance scores, midrange overall attractiveness) to OkCupid's data team. '!"be data team generated a list of possible names. which they passed on to our admin. She then had a listof names, with no other Information auachcd. 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 arrributes were only connected once permission was granted. Notes 253 77 Another long-held idea in network theory Though embeddedness was first proposed by Granoveuer In 1985. my remaining discussion of embeddedness and of mterpcrsonal netv•ork 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 somewhatsimplify their original work for a nonacademic audience. 79 an astounding 75 percent ofthe time Backstrom and Kleinberg define many subtly different mathemar!cal kinds of dispersion. My number here refers to the accuracy they reponed with the method they call "recursiVe dispersion." 79 50 percent mort' likely This Is drawn from the foUowing passage in Backsrrom and Kleinberg's paper: "We find that relationships on which recursive dispersion fails tO correctly tdemtfy the partner are significantly more likely to cransltlon to 'single' status [that Is. break up] over a60-day period. This effect holds across all relationship age~ 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 d1e partner." 80 Have a meeting with Microsoft people This might not be broadly true of all Microsoft employees; however. the teams responsible lor Mtcrosoft's mobile and tablet products are. m my experience, dogfooders of the first order. Windows mobile is so rare as to be especially nmewormy. so you remember It when you see ic. This is a good place to poim out that I am a lifelong user of Microsoft Office. and all the chans and much of the analysts in this book were done in Excel. Chapter 5 There's No Success L•l(e Failure 86 one ofCoogle's best desig11ers Douglas Bowman leaving Google Is a famous event In tech drcles. See h•s own post "Goodbye. Google" at swpdestgn .com/archive/2009/03/'2JJ/goodbye-google.hunl. 88 no evidence ofpeople 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-anonymtze the phoros. However. these apps never caught on. mostly because they were difficult to use and even then only worked part of the time.These unscram- 258 Notes biers 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 phmo, licensed from Gerry Images. Chapter 6: The Confounding Factor 99 of a certain type See, for example. "Blacks Sull Dytng More from Cancer Than Whites." by Jordan Ute, Scientific American. Febmary 2009. Also see the Sentencing Project's "Cnminal Jusrlce Primer for the 11lth Congress." which details many depressing disparities in the sentences handed down to whites. compared to mmority defendanrs: scmencingproject.orgldoc/ publicadons/qprimer'2009.pdf. 100 conclusions like this The headline cited is from ThinkProgress.org. "Srudy: Black Defendants Are at Least 30% More Likely to Be Imprisoned Than White DefendantS for the Same Crime." by lnimai Chetliar. August 30. 2012, thinkprogress.org/justice/2012/08/30/770501Istudy-black-dcfendancs -are-at-leasc-30-more-likcly-to-be-lmprisoned-chan-white-defendanrs-for -the-same-crime. 100 in the 97,000 results It's a btt of a hack to get Google to giVe you a number here. My exact query was for • black quarterback' -adsfTsdada." Using the minus sign with the nonsense word keeps the page from autOmatically reruming images InStead of the "about W.CXXJ results" text. I'm sure wimour the browser in from of you. this all sounds mysnfymg. Try it yourself If you care. and you'll see immediately what I mean. Also. this is another example ofa raw number that has changed durmg the course ofwrillng this book. I've also gotten "89,8(X) results"returned to me. 100 I found only ont' article See Jason Ltsk. "Quarterbacks and Whether Race Maners." The Big Lead. December 2. 2010. meb•glead.com/2010/12/02/ quanerbacks-and-whether-racc-maucrs/. Of course, the fact that I found only one writer who calculates quanerback rating by race Is hardly proof that no ather writer has made the calculation. However. Ispem several hours combing results and found only Lisk. 101 the four largest racial groups 15 percent of OkCuptd users who select an ethniciry select more than one race; 3 percem select a race other than the Notes 259 four largest. These people arc excluded from the analysis. as are people who neglected to choose a race at all. 102 "nonnalize" each row I normalized against the simple average In each f Big Data." Harvard Business Review Blog Network. March 25, 2014. 128 included in 7 million searches a year Srephens-Davtdowltz. "How Rac1st Are We?" 129 more American than "apple pie" Google Trends index for US searches. January 2004-Septernber 2013. for "apple pie": 25. For "nigger": 32. 129 And, tellingly The ratio of "ntgga":"nigger" is thirty times higher in tweets sem from my Twiner corpus than reflected in Google Trends. That is. on T\vitter ·nigger" appears thirty times less frequently. 130 roughly 1 in 100 searches for "Obama· Srephens-DaVldowirz shared this fact \vlrh me over e-mail. 130 25 percent below the pre-Obama status quo Stephens-Davtdowlrz. "How Racist Are We?" This is also confirmable firsthand through Google Trends. 131 Other awful tenns These radal epithets are far less common on Twiuer, in private messages co OkCuptd. and in Google search. as conf1rmed by Srephens-Davidmvtrz via e-ma1l. 131 Ifyou'rc notfamiliar with autocomplete The algorithm that supplies Google Notes 263