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Wednesday, October 24, 2018

Who Controls Your Facebook Feed

Every time you open Facebook, one of the world’s most influential, controversial, and misunderstood algorithms springs into action. It scans and collects everything posted in the past week by each of your friends, everyone you follow, each group you belong to, and every Facebook page you’ve liked. For the average Facebook user, that’s more than 1,500 posts. If you have several hundred friends, it could be as many as 10,000. Then, according to a closely guarded and constantly shifting formula, Facebook’s news feed algorithm ranks them all, in what it believes to be the precise order of how likely you are to find each post worthwhile. Most users will only ever see the top few hundred.No one outside Facebook knows for sure how it does this, and no one inside the company will tell you. And yet the results of this automated ranking process shape the social lives and reading habits of more than 1 billion daily active users—one-fifth of the world’s adult population. The algorithm’s viral power has turned the media industry upside down, propelling startups like BuzzFeed and Vox to national prominence while 100-year-old newspapers wither and die. It fueled the stratospheric rise of billion-dollar companies like Zynga and LivingSocial—only to suck the helium from them a year or two later with a few adjustments to its code, leaving behind empty-pocketed investors and laid-off workers. Facebook’s news feed algorithm can be tweaked to make us happy or sad; it can expose us to new and challenging ideas or insulate us in ideological bubbles.
And yet, for all its power, Facebook’s news feed algorithm is surprisingly inelegant, maddeningly mercurial, and stubbornly opaque. It remains as likely as not to serve us posts we find trivial, irritating, misleading, or just plain boring. And Facebook knows it. Over the past several months, the social network has been running a test in which it shows some users the top post in their news feed alongside one other, lower-ranked post, asking them to pick the one they’d prefer to read. The result? The algorithm’s rankings correspond to the user’s preferences “sometimes,” Facebook acknowledges, declining to get more specific. When they don’t match up, the company says, that points to “an area for improvement.”

“Sometimes” isn’t the success rate you might expect for such a vaunted and feared bit of code. The news feed algorithm’s outsize influence has given rise to a strand of criticism that treats it as if it possessed a mind of its own—as if it were some runic form of intelligence, loosed on the world to pursue ends beyond the ken of human understanding. At a time when Facebook and other Silicon Valley giants increasingly filter our choices and guide our decisions through machine-learning software, when tech titans like Elon Musk and scientific laureates like Stephen Hawking are warning of the existential threat posed by A.I., the word itself—algorithm—has begun to take on an eerie affect. Algorithms, in the popular imagination, are mysterious, powerful entities that stand for all the ways technology and modernity both serve our every desire and threaten the values we hold dear.
The reality of Facebook’s algorithm is somewhat less fantastical, but no less fascinating. I had a rare chance recently to spend time with Facebook’s news feed team at their Menlo Park, California, headquarters and see what it actually looks like when they make one of those infamous, market-moving “tweaks” to the algorithm—why they do it, how they do it, and how they decide whether it worked. A glimpse into its inner workings sheds light not only on the mechanisms of Facebook’s news feed, but on the limitations of machine learning, the pitfalls of data-driven decision making, and the moves Facebook is increasingly making to collect and address feedback from individual human users, including a growing panel of testers that are becoming Facebook’s equivalent of the Nielsen family.
Facebook’s algorithm, I learned, isn’t flawed because of some glitch in the system. It’s flawed because, unlike the perfectly realized, sentient algorithms of our sci-fi fever dreams, the intelligence behind Facebook’s software is fundamentally human. Humans decide what data goes into it, what it can do with that data, and what they want to come out the other end. When the algorithm errs, humans are to blame. When it evolves, it’s because a bunch of humans read a bunch of spreadsheets, held a bunch of meetings, ran a bunch of tests, and decided to make it better. And if it does keep getting better? That’ll be because another group of humans keeps telling them about all the ways it’s falling short: us.
When I arrive at Facebook’s sprawling, Frank Gehry–designed office in Menlo Park, I’m met by a lanky 37-year-old man whose boyish countenance shifts quickly between an earnest smile and an expression of intense focus. Tom Alison is director of engineering for the news feed; he’s in charge of the humans who are in charge of the algorithm.

Alison steers me through a maze of cubicles and open minikitchens toward a small conference room, where he promises to demystify the Facebook algorithm’s true nature. On the way there, I realize I need to use the bathroom and ask for directions. An involuntary grimace crosses his face before he apologizes, smiles, and says, “I’ll walk you there.” At first I think it’s because he doesn’t want me to get lost. But when I emerge from the bathroom, he’s still standing right outside, and it occurs to me that he’s not allowed to leave me unattended.  
For the same reason—Facebook’s fierce protection of trade secrets—Alison cannot tell me much about the actual code that composes the news feed algorithm. He can, however, tell me what it does, and why—and why it’s always changing. He starts, as engineers often do, at the whiteboard.

“When you study computer science, one of the first algorithms you learn is a sorting algorithm,” Alison says. He scribbles a list of positive integers in dry erase:


4, 1, 3, 2, 5

The simple task at hand: devise an algorithm to sort these numbers into ascending order. “Human beings know how to do this,” Alison says. “We just kind of do it in our heads.”

Computers, however, must be told precisely how. That requires an algorithm: a set of concrete instructions by which a given problem may be solved. The algorithm Alison shows me is called “bubble sort,” and it works like this:

  1. For each number in the set, starting with the first one, compare it to the number that follows, and see if they’re in the desired order.
  2. If not, reverse them.
  3. Repeat steps 1 and 2 until you’re able to proceed through the set from start to end without reversing any numbers.
he virtue of bubble sort is its simplicity. The downside: If your data set is large, it’s computationally inefficient and time-consuming. Facebook, for obvious reasons, does not use bubble sort. It does use a sorting algorithm to order the set of all posts that could appear in your news feed when you open the app. But that’s the trivial part—a minor subalgorithm within the master algorithm. The nontrivial part is assigning all those posts a numerical value in the first place. That, in short, is the job of the news feed ranking team: to devise a system capable of assigning any given Facebook post a “relevancy score” specific to any given Facebook user.

That’s a hard problem, because what’s relevant to you—a post from your childhood friend or from a celebrity you follow—might be utterly irrelevant to me. For that, Alison explains, Facebook uses a different kind of algorithm, called a prediction algorithm. (Facebook’s news feed algorithm, like Google’s search algorithm or Netflix’s recommendation algorithm, is really a sprawling complex of software made up of smaller algorithms.)

“Let’s say I ask you to pick the winner of a future basketball game, Bulls vs. Lakers,” Alison begins. “Bulls,” I blurt. Alison laughs, but then he nods vigorously. My brain has taken his input and produced an immediate verbal output, perhaps according to some impish algorithm of its own. (The human mind’s algorithms are far more sophisticated than anything Silicon Valley has yet devised, but they’re also heavily reliant on heuristics and notoriously prone to folly.)

Random guessing is fine when you’ve got nothing to lose, Alison says. But let’s say there was a lot of money riding on my basketball predictions, and I was making them millions of times a day. I’d need a more systematic approach. “You’re probably going to start by looking at historical data,” he says. “You’re going to look at the win-loss record of each team, the records of the individual players, who’s injured, who’s on a streak.” Maybe you’ll take into account environmental factors: Who’s the home team? Is one squad playing on short rest, or after a cross-country flight? Your prediction algorithm might incorporate all of these factors and more. If it’s good, it will not only predict the game’s winner, but tell you its degree of confidence in the result.
That’s analogous to what Facebook’s news feed algorithm does when it tries to predict whether you’ll like a given post. I ask Alison how many variables—”features,” in machine-learning lingo—Facebook’s algorithm takes into account. “Hundreds,” he says.

It doesn’t just predict whether you’ll actually hit the like button on a post based on your past behavior. It also predicts whether you’ll click, comment, share, or hide it, or even mark it as spam. It will predict each of these outcomes, and others, with a certain degree of confidence, then combine them all to produce a single relevancy score that’s specific to both you and that post. Once every possible post in your feed has received its relevancy score, the sorting algorithm can put them in the order that you’ll see them on the screen. The post you see at the top of your feed, then, has been chosen over thousands of others as the one most likely to make you laugh, cry, smile, click, like, share, or comment.

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