In late March, food delivery company Eat24 received a barrage of press when it published an open letter announcing it was quitting Facebook. The reason? Because, according to the company (and echoed by many Facebook marketers), Facebook was constantly tweaking its algorithm to decrease organic reach for brand pages, thereby forcing them to cough up more and more money to reach the users who like their pages.
But Slate’s Will Oremus sat down with Facebook’s Will Cathcart to discuss the machine learning algorithms of the Newsfeed. According to Facebook, users are more addicted to the platform than ever, and that’s because Facebook attempts to ascertain not only what content they say they like (by clicking the Like button), but the content they actually like (links they actually click on and read before returning to the site):
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.
Three sources of data in particular are helping Facebook to refashion its news feed algorithms to show users the kinds of posts that will keep them coming back: surveys, A/B tests, and data on the time users spend away from Facebook once they click on a given post—and what they do when they come back.
Surveys can get at questions that other metrics can’t, while A/B tests offer Facebook a way to put its hunches under a microscope. Every time its developers make a tweak to the algorithms, Facebook tests it by showing it to a small percentage of users. At any given moment, Cathcart says, there might be 1,000 different versions of Facebook running for different groups of users. Facebook is gathering data on all of them, to see which changes are generating positive reactions and which ones are falling flat.