Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

Muslims are the only group stereotyped as terrorists. When a Muslim American plays into this stereotype, the response can be instantaneous and vicious. Google search data can give us a minute-by-minute peek into such eruptions of hate-fueled rage.

Consider what happened shortly after the mass shooting in San Bernardino, California, on December 2, 2015. That morning, Rizwan Farook and Tashfeen Malik entered a meeting of Farook’s coworkers armed with semiautomatic pistols and semiautomatic rifles and murdered fourteen people. That evening, literally minutes after the media first reported one of the shooters’ Muslim-sounding name, a disturbing number of Californians had decided what they wanted to do with Muslims: kill them.

The top Google search in California with the word “Muslims” in it at the time was “kill Muslims.” And overall, Americans searched for the phrase “kill Muslims” with about the same frequency that they searched for “martini recipe,” “migraine symptoms,” and “Cowboys roster.” In the days following the San Bernardino attack, for every American concerned with “Islamophobia,” another was searching for “kill Muslims.” While hate searches were approximately 20 percent of all searches about Muslims before the attack, more than half of all search volume about Muslims became hateful in the hours that followed it.

And this minute-by-minute search data can tell us how difficult it can be to calm this rage. Four days after the shooting, then-president Obama gave a prime-time address to the country. He wanted to reassure Americans that the government could both stop terrorism and, perhaps more important, quiet this dangerous Islamophobia.

Obama appealed to our better angels, speaking of the importance of inclusion and tolerance. The rhetoric was powerful and moving. The Los Angeles Times praised Obama for “[warning] against allowing fear to cloud our judgment.” The New York Times called the speech both “tough” and “calming.” The website Think Progress praised it as “a necessary tool of good governance, geared towards saving the lives of Muslim Americans.” Obama’s speech, in other words, was judged a major success. But was it?

Google search data suggests otherwise. Together with Evan Soltas, then at Princeton, I examined the data. In his speech, the president said, “It is the responsibility of all Americans—of every faith—to reject discrimination.” But searches calling Muslims “terrorists,” “bad,” “violent,” and “evil” doubled during and shortly after the speech. President Obama also said, “It is our responsibility to reject religious tests on who we admit into this country.” But negative searches about Syrian refugees, a mostly Muslim group then desperately looking for a safe haven, rose 60 percent, while searches asking how to help Syrian refugees dropped 35 percent. Obama asked Americans to “not forget that freedom is more powerful than fear.” Yet searches for “kill Muslims” tripled during his speech. In fact, just about every negative search we could think to test regarding Muslims shot up during and after Obama’s speech, and just about every positive search we could think to test declined.

In other words, Obama seemed to say all the right things. All the traditional media congratulated Obama on his healing words. But new data from the internet, offering digital truth serum, suggested that the speech actually backfired in its main goal. Instead of calming the angry mob, as everybody thought he was doing, the internet data tells us that Obama actually inflamed it. Things that we think are working can have the exact opposite effect from the one we expect. Sometimes we need internet data to correct our instinct to pat ourselves on the back.

So what should Obama have said to quell this particular form of hatred currently so virulent in America? We’ll circle back to that later. Right now we’re going to take a look at an age-old vein of prejudice in the United States, the form of hate that in fact stands out above the rest, the one that has been the most destructive and the topic of the research that began this book. In my work with Google search data, the single most telling fact I have found regarding hate on the internet is the popularity of the word “nigger.”

Either singular or in its plural form, the word “nigger” is included in seven million American searches every year. (Again, the word used in rap songs is almost always “nigga,” not “nigger,” so there’s no significant impact from hip-hop lyrics to account for.) Searches for “nigger jokes” are seventeen times more common than searches for “kike jokes,” “gook jokes,” “spic jokes,” “chink jokes,” and “fag jokes” combined.

When are searches for “nigger(s)”—or “nigger jokes”—most common? Whenever African-Americans are in the news. Among the periods when such searches were highest was the immediate aftermath of Hurricane Katrina, when television and newspapers showed images of desperate black people in New Orleans struggling for their survival. They also shot up during Obama’s first election. And searches for “nigger jokes” rise on average about 30 percent on Martin Luther King Jr. Day.

The frightening ubiquity of this racial slur throws into doubt some current understandings of racism.

Any theory of racism has to explain a big puzzle in America. On the one hand, the overwhelming majority of black Americans think they suffer from prejudice—and they have ample evidence of discrimination in police stops, job interviews, and jury decisions. On the other hand, very few white Americans will admit to being racist.

The dominant explanation among political scientists recently has been that this is due, in large part, to widespread implicit prejudice. White Americans may mean well, this theory goes, but they have a subconscious bias, which influences their treatment of black Americans. Academics invented an ingenious way to test for such a bias. It is called the implicit-association test.

The tests have consistently shown that it takes most people milliseconds longer to associate black faces with positive words, such as “good,” than with negative words, such as “awful.” For white faces, the pattern is reversed. The extra time it takes is evidence of someone’s implicit prejudice—a prejudice the person may not even be aware of.

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