A Thousand Points of Data
A week after the election (in which about 42% of eligible voters didn’t vote), some people who voted for Donald Trump are upset. They ask their fellow citizens not to insult them by calling them “racist” or “bigot.” They remind their fellow citizens that generalizations are often incorrect when applied to individuals. At the same time, some people who opposed Donald Trump’s candidacy point out that Trump himself made insulting generalizations about Muslims, Mexican immigrants, and other groups —yet their fellow citizens voted for him. Those Trump opponents answer with a kind of corollary to the Golden Rule: “If you accept the doing unto others of that which you would not like to have done onto you, don’t be surprised when those others respond in kind.”
But the various groups are united by one sentiment: They say that they should not be defined by a single data point (that they voted for Trump, for example; or that they are Muslim; or that they are gay; etc.). Yet, in the wake of the election, dozens of analyses have been written, trying to make meaning out of data by splitting the electorate, looking, precisely, at single data points: how women voted, versus how men did; black versus non-black; college-educated v. non-college-educated; evangelical v. Catholic; married v. unmarried; younger v. older.
When it comes to complex truths about complex issues in multifaceted societies, data can become “factiness” and muddle—rather than clarify—the complex truth. As sociologist Nathan Jurgenson wrote in a recent blog post coining the term, “factiness is obsessing over and covering ourselves in fact after fact while still missing bigger truths.” If you are, say, a non-college-educated second-generation Mexican-American married to a Muslim woman who is opposed to abortion rights, how did you vote, and for what reason(s)? And were you influenced by Clinton? Comey? Social media filter bubbles? Party affiliation? Fake news? Your health care costs? All of those?
We woke up last Wednesday morning and rubbed our eyes, looking out at our fellow citizens, trying to find out more about them and their vote. The internet was supposed to have helped tell us. Back in its early days, people were excited about the fact that the internet allowed them to reach out across great distances, communicate (cheaply, quickly, and easily) with people very different from themselves, and find and join like-minded online communities… Pause on that last one for a second: “like-minded communities.”
Then came the effort to “personalize” the information that we got online—both in the media and in the searches that we ran. Do you read something with a particular political slant? We’ll give you more of the same! Do you read about a particular issue? We’ll show you more on that. You might become fixated on that one issue and miss many others that might be important to your fellow Americans, but hey, you were interested!
We are a tapestry of complex people facing complex issues, yet somehow it was decided that if you’re a green thread you should only be shown the green threads, if you’re a purple only be shown the purple—missing the bigger picture, and missing the extremely intricate ways in which we are interwoven with all the other strands. The post-election analyses are still pulling at the threads.
Get rid of the personalization. Demand to be shown the whole picture. Draw strength from “like-minded communities,” but then come out of them to talk to others who are (as Maya Angelou put it) unalike. And speak out, online and off, when any of us are being labeled on the basis of a single data point. Push back against bigotry, counter misinformation, and fight “factiness,” too.
Photo by Hugh Grew, used without modification under a Creative Commons license.