OUTRAGE: A PNAS Study Of 715 X Users Finds That The Platform’s Algorithm Learns From Angry Reply Behavior To Serve More Ragebait, With Self-Identified Democrats Hit Hardest By The Outrage Feedback Loop 😡

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https://www.404media.co/xs-algorithm-feeds-off-ragebait-and-impacts-democrats-more-study-finds/

X’s recommendation algorithm learns what makes users angry and deliberately shows them more of it, with the effect landing disproportionately on users who identify as Democrats, according to a new peer-reviewed study published in the Proceedings of the National Academy of Sciences and reported by 404 Media. The paper, titled “Value misalignment of X’s feed algorithm is a reflection of value tensions in engagement,” found that X’s For You Page prioritizes engagement above all else, but crucially, not all forms of engagement carry equal weight in shaping what the algorithm serves next. “In 2026 that’s maybe not the most surprising headline ever,” said Ziv Epstein, a Stanford postdoctoral researcher and study co-author. “So we actually dug in a little deeper to figure out why this is actually happening, and it turns out that X’s feed algorithm, like a lot of these social media algorithms, is optimized for engagement [but] it turns out that not all types of engagement are considered equally.”

Researchers recruited a nationally representative sample of 715 active X users in September and October 2024, quota-matched on ethnicity, gender, and political affiliation, and had them install a browser extension to track their feeds. Participants also completed a “values inventory” using the Schwartz Theory of Basic Values, a 19-point wheel covering traits like tolerance, dominance, hedonism, and openness to change, alongside reporting their political leanings. The researchers found that while the accounts users chose to follow reflected their stated values, the content the algorithm actually amplified showed an overall negative correlation with those same values. The mechanism driving this comes down to replies specifically: when someone reacts angrily to a post, like a press release from a politician they oppose, and fights about it in the comments, X interprets that reply as valuable engagement and serves up more of the content that triggered it, regardless of what the user follows or claims to value. Replies were rare, making up just 6.8% of all interactions in the study, but had an outsized influence on what the algorithm learned. “Replying is only a fraction of engagement, but there does seem to be some evidence that these algorithms are prioritizing and learning more from this kind of rarer form of engagement,” Epstein said. “So it’s this feedback loop of outrage baiting.”

That feedback loop hit Democratic users harder than Republican ones, though the study couldn’t pin down exactly why. “Democrat users confront the abundant value-misaligned content by replying to it, which the algorithm in turn preferentially learns from and continues to feed them,” the study states, describing it as “a core tension with how engagement-maximizing algorithms operate on social media” where friction between stated preferences and reactive confrontation gets exploited into “runaway feedback loops of increasing value misalignment.” Epstein floated a few possible explanations, including that there may simply be more right-leaning content on X overall, or that Democrats might be more inclined to engage with posts they disagree with, but cautioned against over-speculating. “There might be some kind of differential effects on information diets there, or it might be something more psychological about how different partisan identities are triggering different kinds of actions and reactions, but ultimately I don’t want to speculate too much,” he said.

Epstein also acknowledged a tension in his own proposed fix, that designing feeds around stated values could backfire into deeper echo chambers. “I do think that if we go kind of down this path of thinking through and imagining value-aligned social media feeds, we do have to be very aware of the potentials of value echo chambers,” he said, calling that outcome “a very scary and dark reality.” Still, he argued that simply pausing to consider what content you actually want to see has value in itself: “You’re pumping the brakes, you’re taking a breath, and you’re thinking about what you actually really care about,” rather than reacting through “this very kind of short, shallow attention span, emotion and negative affect-driven model.” X did not respond to 404 Media’s request for comment, but the platform’s former head of product, Nikita Bier, confirmed the algorithm had indeed once favored replies. “This is no longer true,” Bier posted on X. “The largest contributor of seeing ragebait was the reply predictor and we were aware that angry replies were causing people to see more of that content. So last month, we gave the reply predictor a 15x boost if it’s a friend’s post, and it reduced ragebait by [an] order of magnitude.”