We all know by now that Facebook isn’t cool.
And yet somehow it's more popular than ever. On Wednesday the company announced that its growth continues to surge—not only in terms of the sheer number of Facebook users, but in terms of how much they use the site. On any given day, Mark Zuckerberg said, 63 percent of Facebook’s 1.28 billion users log into the site. And the proportion of users who log in at least six days a week has now surpassed 50 percent.
How is it possible that Facebook keeps getting more addictive over time, rather than less?
It’s possible because Facebook knows what you like—and it’s getting better at understanding you all the time.
As much work and data—your data—as Facebook feeds into its targeted advertising, it works at least as hard at figuring out which of your friends’ posts you’re most likely to want to see each time you open the app. Advertisers may butter Facebook’s bread, but its most pressing interest of all is in keeping its users coming back for more. If it ever fails at that, its advertising business will implode.
So how does Facebook know what we like? On a recent visit to the company’s headquarters in Menlo Park, I talked about that with Will Cathcart, who oversees the product management teams that work on the company’s news feed. The answer holds lessons for the future of machine learning, the media, and the Internet at large.
Facebook launched the news feed in 2006, but it didn’t introduce the “like” button until a year later. Only then did the site have a way to figure out which posts you were actually interested in—and which new posts you might be interested in, based on what your friends and others were liking. In the years since its launch, the news feed has gone from being a simple chronological list to a machine learning product, with posts ranked in your timeline according to the likelihood that you would find them interesting. The goal is to ensure that, for example, the first picture of your best friend’s new baby would take precedence over a remote acquaintance’s most recent Mafia Wars score.
For a while, Facebook likes—coupled with a few other metrics, like shares, comments, and clicks—served as a pretty decent proxy for engagement. But they were far from perfect, Cathcart concedes. A funny photo meme might get thousands of quick likes, while a thoughtful news story analyzing the conflict in Ukraine would be punished by Facebook’s algorithms because it didn’t lend itself to a simple thumbs-up. The result was that people’s news feeds became littered with the social media equivalent of junk food. Facebook had become optimized for stories that people Facebook-liked, rather than stories that people actually liked.
Worse, many of the same stories that thousands of people Facebook-liked turned out to be ones that thousands of other people genuinely hated. They included posts that had clicky headlines designed to score cheap likes and clicks, but that actually led to pages filled with spammy ads rather than the content that the headline promised. But in the absence of a “dislike” button, Facebook’s algorithms had no way of knowing which posts were turning users off. Eventually, about a year ago, Facebook acknowledged that it had a “quality content” problem.
This is not a problem specific to Facebook. It’s a problem that confronts every company or product that harnesses data analytics to drive decision-making. So how do you solve it? For some, the answer might be to temper data-driven insights with a healthy dose of human intuition. But Facebook’s news feed operates on a scale and a level of personalization that makes direct human intervention infeasible. So for Facebook, the answer was to begin collecting new forms of data designed to generate insights that the old forms of data—likes, shares, comments, and clicks—couldn’t.
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