How I Tried to Defy the Facebook Algorithm

Facebook is often sarcastically described as a platform for sharing baby pictures. When I log in, I do see some of those—but many of the babies in my feed are baby rats.

The story of how these hairless pups started populating my News Feed began when a friend told me about a Facebook group—a lively forum for discussing baby names—that had captivated her. I joined too, and soon afterward my timeline was peppered with requests for “a strong Irish name” or “a name similar to Everly which we unfortunately can’t use.”

I’m not currently in the market for a baby name, but the novelty was welcome. So I sought out more: I joined groups or liked pages for wine lovers, slow runners, appreciators of dinosaurs, southeastern-Michigan snow obsessives. Eventually, I found Everything Rat Breeding.

Before I started this experiment in joinerism, using Facebook felt like watching an algorithmically conducted parade of the lifestyles, accomplishments, and worldviews of my peers. The experience seemed calculated to produce envy and insecurity. But the pictures of dinosaur fossils and reviews of wines with “masculine fruit” transformed that procession into a bizarre and occasionally delightful show, affording me glimpses of all the things there are to care about beyond what preoccupies my particular social circle.

I liked the variety; it made browsing feel less competitive. And witnessing strangers go on about some mundane subject that mattered deeply to them was oddly engrossing. The new occupants of my News Feed were giving me a break from personalization that I didn’t know I needed.

According to Ethan Zuckerman, the director of MIT’s Center for Civic Media, most of today’s social networks are predicated on bringing people’s offline relationships online. And since offline networks are shaped by homophily—people’s tendency to cluster with others who are like them—social networks, too, tend to surround users with the types of people they already know. Homophily is an ancient human instinct, but Facebook’s algorithm reinforces it with industrial efficiency.

What I was trying to do, then, was stop Facebook from doing what it is inherently good at, and hack it to give me the reverse: serendipity, surprise, heterophily. Soon I started to wonder: Were there other, better ways to do this?

Throwing the algorithm off my scent got easier when I enlisted the help of other algorithms. I did this with a tool called Noisify, which populates one’s Facebook search bar with random words. Cathy Deng, a programmer in San Francisco, built Noisify after the 2016 election, when people she knew seemed obsessed with political “filter bubbles.” “It feels very limiting to be like, ‘The entire world exists on this axis of left-versus-right,’ ” she told me. “The way I was seeing it was: The world is so much richer than that.”

Installing Noisify supercharged my pursuit of novelty. The tool has directed me to corners of Facebook that feel like mini-vacations from the usual onslaught of life updates and political news: pages devoted to intellectual-property rights, items for sale in small-town Maryland, and the apparently beloved 20th-century organist Virgil Fox.

This approach to browsing can work on other social-media platforms, too. “Every month or so,” says Crystal Abidin, an anthropologist who researches internet culture, “I selectively follow a bunch of accounts—sometimes to do with a specific country or demographic of people or culture—on Instagram, in a bid to change up my feed.” To achieve the same effect on YouTube, she’ll binge-watch random videos.

Abidin’s browsing is often for research purposes, and she wants to be able to survey a wide swath of the digital landscape instead of just the slice of it that social-media platforms tailor to her personal characteristics. She finds it useful to scroll through images on Instagram with hashtags that “are basically not very viable, because there are too many posts archived in them,” like #japan or #babies. She says it’s “bewildering, sometimes fun, but also really scary that there’s just so much out there I would never be able to discover.”

Max Hawkins, a 28-year-old programmer, elevated the goal of subverting algorithms to a way of life. After graduating from college in 2013 and getting a job at Google, Hawkins grew restless and sought ways to make his life more interesting. He built a tool that had Uber drop him off at random locations around the Bay Area. Then he built a tool that picked random publicly listed Facebook events for him to attend.

Hawkins found the variety refreshing, and after two years, he left his job. Every few months, he let a computer pick the city he would live in, based on airfare, cost-of-living estimates, and his projected income as a freelance programmer. He tried listening to music picked randomly by Spotify, wearing clothes bought randomly on Amazon, growing out random styles of facial hair, and arranging phone calls with friends on randomly selected topics.

Hawkins even chose to get a random tattoo based on a random image search. His commitment to the experiment was steadfast and somewhat terrifying. “I was really worried it was going to be anime porn or something,” he said, “and I’d be stuck with it for the rest of my life.” But the computer’s selection was an abstract illustration of a parent and child. “I super lucked out,” he said. (Other outcomes of his experiments were, perhaps inevitably, less positive—like the time the computer told him to grow a soul patch.)

Hawkins was living out a sort of algorithmic jujitsu, using his own code to redirect recommendation engines toward the unexpected. “The nice thing about randomness is that it can give you something that is completely outside of what you would even imagine,” he told me. “And one place that computers can benefit us is that they have such a wider range of things that they can be aware of.”

Unlike Hawkins, I couldn’t fathom letting randomness rule my life. But when he put it like that, I felt a bit sad. In the greater scheme of things, so little computing power is harnessed to promote variety—and so much is channeled toward predictability.

Facebook’s algorithm didn’t seem to know what to make of my newfound penchant for randomness. Once, I joined a group for python fanatics, and Facebook recommended that I check out Reptile Connection. I joined Dinosaurs, and the site suggested Paleo World. At times, my quest felt like pushing together two magnets of the same charge; I sought something different, but was steered toward more of what I’d already seen.

Facebook, Instagram, and other personalized platforms simply aren’t built for what Deng, Abidin, Hawkins, and I were doing. Their business model—targeting advertisements based on users’ demonstrated preferences—depends on anticipating what will be relevant to people and using that information to entice them to buy products and services. Showcasing random interests wouldn’t serve that goal.

The web hasn’t always been structured like this. In the 1980s and ’90s, Zuckerman told me, before platforms like MySpace and Friendster began to map the internet around our preexisting social lives, networks were usually organized by topic, with no algorithm steering you to what it thought you’d like. This older internet, he noted, was homophilous as well, with a highly educated, demographically narrow user base. But it was typical, say, for three cat lovers to have a conversation across three different continents. People frequently had meaningful interactions with strangers.

Among today’s major platforms, Reddit seems like the most direct heir to these early-internet networks, oriented as it is toward serendipitous and surprising niches. Facebook’s groups seem like a throwback in that sense, too. Jennifer Dulski, the head of Facebook Groups, told me, “When people join groups and they meet other people who care about the same things that they do—maybe they live in the same neighborhood, or they’re both military parents, or they both love mountain climbing, or they both own a corgi; the list goes on—it’s like they’re finding their hidden soulmates.”

Every time I joined a group, however, Facebook inferred that my new groupmates were “people like me”—that I shared their interests, in other words, rather than being interested in them precisely because I do not. Even in its groups, Facebook is not designed to expand your social horizons; as Zuckerman noted, it’s designed to surface things that are relevant to you and your offline peers. Groups are just another way of signaling what those things might be.

The more one pushes back against personalization, the closer one gets to Max Hawkins’s unfiltered, randomized extreme, and all the delight, danger, and drudgery it entails. This can be inefficient and exhausting: There’s a reason Chatroulette, the video-chat app that connects users to random strangers, doesn’t have the same market share as Skype.

My fellow serendipity seekers had started to feel that exhaustion, it turns out, before I even met them. While Abidin still pursues variety as part of her job, Deng now spends less time on social media, and Hawkins has given up the platforms altogether. He says he’s more interested in building his own networks than tinkering with others’.

It’s certainly possible to battle the algorithms and discover new vistas from which to assess one’s life—but the fight becomes wearying. After a while, the baby rats start to look like, well, baby rats.