Stanford Researchers Show A Simple Feed Tweak On X Can Reduce Politica

Stanford University researchers may have hit on something surprisingly effective: a small change in how posts appear on X (formerly Twitter) can make people less hostile toward the other side of the political divide. And the twist is that nothing is censored. Nothing is deleted. The posts are simply pushed a little lower in the feed. The study, run during the heated 2024 US election season, used a browser-based tool that sat on top of X’s existing algorithm. Around 1,200 volunteers installed it and continued using X as usual for ten days.

The tool scanned their timelines for posts that contained extreme rhetoric, partisan hostility, or anti-democratic sentiments. Instead of blocking them, it quietly shifted those posts further down the feed so they didn’t appear front and centre. ALSO READ: Incognito Isn’t Full Privacy: How To Completely Wipe Hidden Browsing Traces At the conclusion of the experiment, both liberal and conservative participants reported significantly warmer attitudes toward people on opposing political sides—significantly more so than participants in the control group, who witnessed unchanging levels of... The key insight is simple: posts at the top of your feed determine your emotional baseline. When content that triggers emotions no longer hits first, temperatures typically decrease, and posts can still be seen by scrolling—they just lose some of their power due to being directly in your face.

A web-based method was shown to mitigate political polarization on X by nudging antidemocratic and extremely negative partisan posts lower in a user’s feed. The tool, which is independent of the platform, has the potential to give users more say over what they see on social media.iStock A new tool shows it is possible to turn down the partisan rancor in an X feed — without removing political posts and without the direct cooperation of the platform. The study, from researchers at the University of Washington, Stanford University and Northeastern University, also indicates that it may one day be possible to let users take control of their social media algorithms. The researchers created a seamless, web-based tool that reorders content to move posts lower in a user’s feed when they contain antidemocratic attitudes and partisan animosity, such as advocating for violence or jailing supporters... Researchers published their findings Nov.

27 in Science. A new Stanford-led study is challenging the idea that political toxicity is simply an unavoidable element of online culture. Instead, the research suggests that the political toxicity many users encounter on social media is a design choice that can be reversed. Researchers have unveiled a browser-based tool that can cool the political temperature of an X feed by quietly downranking hostile or antidemocratic posts. Remarkably, this can occur without requiring any deletions, bans, or cooperation from X itself. The study offers the takeaway that algorithmic interventions can meaningfully reduce partisan animosity while still preserving political speech.

It also advances a growing movement advocating user control over platform ranking systems and the algorithms that shape what they see, which were traditionally guarded as proprietary, opaque, and mainly optimized for engagement rather... The research tool was built by a multidisciplinary team across Stanford, Northeastern University, and the University of Washington, composed of computer scientists, psychologists, communication scholars, and information scientists. Their goal in the experiment was to counter the engagement-driven amplification of divisive content that tends to reward outrage, conflict, and emotionally charged posts, without silencing political speech. Using a large language model, the tool analyzes posts in real time and identifies several categories of harmful political subject matter, including calls for political violence, attacks on democratic norms, and extreme hostility toward... When the system flags such content, it simply pushes those posts lower in the feed so they are less noticeable, like seating your argumentative uncle at the far end of the table during the... A small tweak to your social media feed can make your opponents feel a little less like enemies.

In a new study published in Science, a Stanford-led team used a browser extension and a large language model to rerank posts on X during the 2024 U.S. presidential campaign, showing that changing the visibility of the most hostile political content can measurably dial down partisan heat without deleting a single post or asking the platform for permission. The experiment, run with 1,256 Democrats and Republicans who used X in the weeks after an attempted assassination of Donald Trump and the withdrawal of Joe Biden from the race, targeted a particular kind... The researchers focused on posts that expressed antidemocratic attitudes and partisan animosity, such as cheering political violence, rejecting bipartisan cooperation, or suggesting that democratic rules are expendable when they get in the way of... To reach inside a platform they did not control, first author Tiziano Piccardi and colleagues built a browser extension that quietly intercepted the web version of the X timeline. Every time a participant opened the For you feed, the extension captured the posts, sent them to a remote backend, and had a large language model score each political post on eight dimensions of...

If a post hit at least four of those eight factors, it was tagged as the kind of content most likely to inflame. The tool then reordered the feed for consenting users in real time. In one experiment, it pushed those posts down the feed so participants would need to scroll further to hit the worst material. In a parallel experiment, it did the opposite and pulled that content higher. “Social media algorithms directly impact our lives, but until now, only the platforms had the ability to understand and shape them,” said Michael Bernstein, a professor of computer science in Stanford’s School of Engineering... “We have demonstrated an approach that lets researchers and end users have that power.”

Researchers in the United States have developed a new tool that allows independent scientists to study how social media algorithms affect users—without needing permission from the platforms themselves. The findings suggest that platforms could reduce political polarisation by down-ranking hostile content in their algorithms. The tool, a browser extension powered by artificial intelligence (AI), scans posts on X, formerly Twitter, for any themes of anti-democratic and extremely negative partisan views, such as posts that could call for violence... It then re-orders posts on the X feed in a “matter of seconds,” the study showed, so the polarising content was nearer to the bottom of a user’s feed. The team of researchers from Stanford University, the University of Washington, and Northeastern University then tested the browser extension on the X feeds of over 1,200 participants who consented to having them modified for... American Association for the Advancement of Science (AAAS)

A new experiment using an AI-powered browser extension to reorder feeds on X (formerly Twitter), and conducted independently of the X platform’s algorithm, shows that even small changes in exposure to hostile political content... The findings provide direct causal evidence of the impact of algorithmically controlled post ranking on a user’s social media feed. Social media has become an important source of political information for many people worldwide. However, the platform’s algorithms exert a powerful influence on what we encounter during use, subtly steering thoughts, emotions, and behaviors in poorly understood ways. Although many explanations for how these ranking algorithms affect us have been proposed, testing these theories has proven exceptionally difficult. This is because the platform operators alone control how their proprietary algorithms behave and are the only ones capable of experimenting with different feed designs and evaluating their causal effects.

To sidestep these challenges, Tiziano Piccardi and colleagues developed a novel method that lets researchers reorder people’s social media feeds in real time as they browse, without permission from the platforms themselves. Piccardi et al. created a lightweight, non-intrusive browser extension, much like an ad blocker, that intercepts and reshapes X’s web feed in real time, leveraging large language model-based classifiers to evaluate and reorder posts based on their... This tool allowed the authors to systematically identify and vary how content expressing antidemocratic attitudes and partisan animosity (AAPA) appeared on a user’s feed and observe the effects under controlled experimental conditions. In a 10-day field experiment on X involving 1,256 participants and conducted during a volatile stretch of the 2024 U.S. presidential campaign, individuals were randomly assigned to feeds with heightened, reduced, or unchanged levels of AAPA content.

Piccardi et al. discovered that, relative to the control group, reducing exposure to AAPA content made people feel warmer toward the opposing political party, shifting the baseline by more than 2 points on a 100-point scale. Increasing exposure resulted in a comparable shift toward colder feelings toward the opposing party. According to the authors, the observed effects are substantial, roughly comparable to three years’ worth of change in affective polarization over the duration of the intervention, though it remains unknown if these effects persist... What’s more, these shifts did not appear to fall disproportionately on any particular group of users. These shifts also extended to emotional experience; participants reported changes in anger and sadness through brief in-feed surveys, demonstrating that algorithmically mediated exposure to political hostility can shape both affective polarization and moment-to-moment emotional...

“One study – or set of studies – will never be the final word on how social media affects political attitudes. What is true of Facebook might not be true of TikTok, and what was true of Twitter 4 years ago might not be relevant to X today,” write Jennifer Allen and Joshua Tucker in... “The way forward is to embrace creative research and to build methodologies that adapt to the current moment. Piccardi et al. present a viable tool for doing that.” Reranking partisan animosity in algorithmic social media feeds alters affective polarization

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