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Signaling, Liu argued, had blurred. The expensive-looking shirt was a bargain at H&M. Much of the world Bourdieu charted in
Distinction
had moved online. One's
habitus
could be expressed in the casual Instagram post of the vintage modernist chair passed down from one's grandparents or the richness of the
crema
(a word no one knew a few years ago) in one's single-origin espresso.

The anxious positioning Bourdieu had noted could be felt in a tweeted “humblebrag,” an attempt to claim cultural capital without looking as if one were doing so. Thus the up-and-coming band tweets, “
Our song has just come on the radio in our taxi. Awkward!” People's musical likes, among others, could be displayed in their Facebook profiles. And not idly: One university study of Facebook accounts found that only people who put “classical” and “jazz,” and not “indie” or “dance,” in their “likes” encouraged others to follow suit. Only the former categories had an aura of prestige.

Teasing explanation from all the Hunch data could breed questionable
correlations and fanciful theories. Liu suggested that someone's propensity to walk out of a movie he did not like could be a psychological surrogate for being more predisposed toward divorce. “A bad marriage is like a bad movie,” he told me. “Do you stick around?” At moments like this, it seemed hard to take Hunch as little more than a data-driven gimmick. But then, back in the Decision Lounge (that is, the only enclosed space) at Hunch's offices, Liu ran me through the site's “Twitter Predictor.” Hunch took my Twitter followers, and the people I followed, mapped all their taste coordinates, and then generated one for me. “This is taste by association,” he said.

The Twitter Predictor then asked me questions and guessed how I would answer them. “Given the name of a well-known foreign country, would you know whether their time zone is ahead or behind yours?”
Yes
. “Did you vote in your country's last major election?”
Yes
. “Do you watch documentaries?”
Yes
. So far, the Twitter Predictor had me figured out quite well. I felt as if I were on OkCupid and had found myself.
*
³
But were they obvious questions? Or was I simply falling for the so-called Forer effect, that tendency, in places like psychological tests or fortuneteller readings, to see ourselves sharply revealed in what are actually very broad assertions?

As the questions kept coming, they seemed to get more specific and less naturally aligned by factors like politics: “Do you think giving clean needles to addicts is a good idea?” “Do you play games on Facebook?” “Should doctors be able to assist a patient with suicide?” But it did not falter. Liu checked my score. “Hunch is up 19–0.” He told me they have achieved roughly 90 percent accuracy on predicting answers. As Hunch's founder Chris Dixon put it, “
People in our studies are actually only consistent with
themselves
about 90 percent of the time.”

It was a curious and powerful moment. In an age of individualism, many of us have convinced ourselves that we are complex creatures marching to our own drummers, unable to be pinned down into safe assumptions. “
My own taste reflects my specialness,” summarizes the music
critic Carl Wilson, where “it's always other people following crowds.” But here I was, in the Decision Lounge, seemingly pinned like a butterfly to a fifty-coordinate wall, my preferences clearly outlined in a connect-the-dots pattern. “What's so fascinating is that we aren't capturing your answers to these questions directly,” Liu said. “We're capturing you as a location in taste space.”

Actually, it is even one step removed: Because I had not previously answered any of Hunch's questions, I was being captured simply by the aggregation of all the answers to these questions given by all the people I am following on Twitter. “Taste is a space on a graph,” Liu said. “Someone can inhabit it without necessarily knowing the specifics of what they believe and their experiences.” This underscores the social homophily—that tendency to cluster—discussed in the last chapter: I was not motivated to answer any of these questions a certain way because I was influenced by someone's individual tweet (though “a lot of users,” notes Liu, “suspected we were reading tweets” to make the Twitter Predictor work).
Rather, I was associating with a lot of people on Twitter who were like me to begin with: Birds of a feather tweet together.

Because people are often puzzled by
other
people's tastes, it is easy to accept the maxim “There's no accounting for taste.” “People just assume that tastes are inexplicable,” Liu told me. They will say, “I am unique, just like everyone else.” “Of course there's accounting for taste,” he added. “You have to look for the right features.”

For Bourdieu, one thing stood, above all else, as a shortcut to cracking someone's taste. “
Nothing more clearly affirms one's ‘class,' nothing more infallibly classifies,” he wrote, “than tastes in music.”

What sort of music do you like?

Is there a question that at once seems so reductionist yet so open-ended, so banal yet so freighted with meaning?

But it comes up: In studies of “zero acquaintance,” where people were meant to try to get to know one another, music was the first topic broached (granted, they were college students). It is not just small talk:
People's music preferences are potent in drawing accurate inferences about their personality, or at least the personality they are trying to project.

Likes seem easier to discuss than dislikes. Likes are public, Hugo Liu had told me.
A person's clothes reveal his likes, but not necessarily his dislikes. Dislikes—even though they are so crucial to taste—tend to be private. Sites such as Facebook do not even offer a “dislike” option.
*
⁴
Talking about likes might be a good way to find out if someone could be a possible friend. But discussing dislikes is generally reserved for those already in your social network; Liu compared dislikes to gossip you exchange with friends, a way to groom relationships. Simply expressing your musical preferences depends on any number of factors: who's asking, what you've listened to lately, where you are, what you can remember.

These kinds of questions animate the Echo Nest, a “music intelligence” company in Cambridge, Massachusetts, that is a kind of mash-up between the neighbors MIT and the Berklee College of Music, data geeks playing with music geeks. The essential job of the Echo Nest, owned by Spotify, is to help solve the dilemma of matching people to music in an age when the latter is in virtually inexhaustible supply.

When I arrived at its offices one afternoon, it probably should not have come as a surprise that the very first interaction I had was about musical taste. As I sat down with Glenn McDonald, the company's principal engineer, I asked what was playing on the stereo. In an office where everyone must be bristling with opinions, how could they decide
what
to play? “The rule is ‘anything but Coldplay,' ” he said sardonically. There it was, that line in the sand, delivered half in jest but still able, in one cutting thrust, to divide the population into those who liked Coldplay, those who did not, and those who did not feel strongly either way—but could still perhaps get the joke. Coldplay may be a particularly good litmus test for taste. Type in “Coldplay is,” and Google autocompletes, in this order, “Coldplay is the best band ever” and “Coldplay is the worst band ever.” Much of the venom for Coldplay is no doubt driven by that very adoration. Whatever the reason,
people are taking sides. Take enough of these sides, and you begin to locate “your music”—and
yourself
—on the taste graph.

De gustibus non est disputandum
. There is no disputing taste. The philosopher Roger Scruton counters, “
Clearly no one really believes the Latin maxim. It is precisely over matters of taste that men are most prone to argue.” Music is an exemplar of what the anthropologist Mary Douglas called the “fences or bridges” quality of goods (or taste), unifying people even as it separates them. “It's like religion in a way,” an inveterately hip record store owner in Greenwich Village once told me. “Why do people hate you so much because you like San Francisco psychedelic rock but you don't like Japanese psychedelia?”

Of course, most people not only do not hate people because they like Japanese psychedelia; they probably have no clue what Japanese psychedelia
is
. This points to a curious thing about taste that Bourdieu identified:
The closer people are to each other socially, the more pronounced taste disputes become. The smaller the territory, the more pitched the battle. This is Freud's famous “narcissism of small differences”; those minor variances, “in people who are otherwise alike,” form “the basis of feelings of hostility between them.”

Part of this must simply be because taste depends on knowledge (or at least the display of it). Who but fans of the band Pavement actually care about the contrarian position that
Wowee Zowee
was their best album?
One study, which plotted people's musical taste on a graph, found that the people who like Philip Glass's opera
Einstein on the Beach
were “located” quite close to those who dislike it. Why? Because with a relatively obscure work, disliking something entails actually knowing something about it, which puts you in a social space close to the people who like it. Take something that more people have heard, like Vivaldi's
Four Seasons
, and the social gulf between likers and dislikers grows (as do the
reasons
for disliking). When the gulf becomes large enough, one's dislike might even spill over into a studied kind of appreciation, which itself gains power, and a kind of safety, by its social distance from the things one normally likes. Wrote Bourdieu, “
The horrors of popular kitsch are easier to ‘recuperate' than those of petit-bourgeois imitation.”

What does the music you like say about you? Before coming to the Echo Nest, I had partaken in one of its playful experiments called “What's Your Stereotype?” You enter a few of your favorite musical acts
and are profiled as a “Manic Pixie Dream Girl” or “Vengeance Dad.” (“Based upon your affinity for artists like: Iron Maiden.”) I was dubbed a “Hipster Barista,” which, given that much of my music listening these days occurs in Brooklyn coffee shops, seemed predictive enough. Brian Whitman, the bearded, laid-back co-founder of the Echo Nest, sounded like a latter-day Bourdieu when he told me nothing is more predictive of a person than his music preference. “If all I knew about you was the last five books you read, I probably wouldn't know much,” he says. “But if I knew the last five songs you listened to on a streaming service, I'd probably know a lot about you.”

Films, he suggested, are less predictive. There are fewer of them and fewer consumption opportunities. Genres matter, but there is not the same hairsplitting as with music. “They're more directly social things,” he said. “Your wife will make you watch a movie.” Music is what people do on their own: in the car, with their headphones, via their playlists and customized stations. Preferences for it are strongly personal, and people will talk about “my music” in a way they do not about “my movies.” When people broadcast the bands they like on a social network like Facebook, research indicates they will not necessary influence someone else to like that band.
They may in fact do the opposite.

In an age in which, as the Echo Nest engineer Paul Lamere described it, you can carry “almost all of recorded music in your pocket,” the question of
what to play next
has grown increasingly complex. Many of the people who sign up for trials on music-streaming services, Whitman said, never actually listen to anything. “They see a blank search box. What do you do?” Some people, McDonald suggested, might “listen to that Dave Matthews album, the CD of which is in a box somewhere they haven't unpacked from the last move.” They are happy for forty-two minutes.

And then what? Call it “Search Fright.” You sign up for a service that has everything you could ever want to listen to, and suddenly the prospect of listening to any
one
thing becomes overwhelming. The goal of music “discovery,” as it is called, is to steer listeners through the morass, navigating within the boundaries of the acceptable and through the shoals of disaffection. “How would you distinguish between the ten
million songs you're never going to like, either because they're terrible or because they're something that has no context for you,” McDonald said, “and one of the ten million songs that might be your favorite thing, if only you knew it existed?”

Located on the other side of the computer screen, the Echo Nest faces the “cold start problem” that bedevils all recommendation enterprises: What is the first song I should play for this person whom I do not really know much about? Figuring out what kind of listener you are, the Echo Nest believes—rather than simply knowing what you listened to—is the key to keeping you engaged. It models attributes like “mainstreamness”—how far out do your tastes go compared with those of other services' listeners? Is Radiohead thrillingly experimental for you or about as popular a band as you will listen to?

The Echo Nest began as an effort to understand, through data and machine learning, the vast world of music by merging its two central qualities: how it sounds, and how we talk about it. A few years prior, Whitman had been recording “intelligent dance music” (“the only genre,” he joked, “happy enough to compliment itself in its name”) under the name Blitter. Like many musicians, he was finding it hard to successfully do it “at scale.” That is geek talk for
no one listened to it
. As he recalls, the audience “was out there but hard to find.” How could those fans be discovered and connected? Returning to grad school, he began doing work in natural language processing and thinking back to his original problem. “All these people are writing about music on the Web. There must be some way to automatically figure out what they're saying about it.”