Ai Tool Reveals Social Media Feeds Can Sway Political Attitudes

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... 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... New research from Stanford University demonstrates that algorithm intervention can reduce partisan animosity and control political unrest on X feeds A groundbreaking study from Stanford University has unveiled a new web-based research AI tool capable of significantly cooling down the partisan rhetoric on social media platforms like X, all without the platform’s direct involvement.

The multidisciplinary research, published in the journal Science, not only offers a concrete way to reduce political polarisation but also paves the path for users to gain more control over the proprietary algorithms that... The researchers sought to counter the toxic cycle where social media algorithms often amplify emotionally charged, divisive content to maximise user engagement. The developed tool acts as a seamless web extension, leveraging a large language model (LLM) to scan a user’s X feed for posts containing anti-democratic attitudes and partisan animosity. This harmful content includes things like advocating for violence or extreme measures against the opposing party. Instead of removing the content, the AI tool simply reorders the feed, pushing these incendiary posts lower down the timeline. 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

New research shows the impact that social media algorithms can have on partisan political feelings, using a new tool that hijacks the way platforms rank content. How much does someone’s social media algorithm really affect how they feel about a political party, whether it’s one they identify with or one they feel negatively about? Until now, the answer has escaped researchers because they’ve had to rely on the cooperation of social media platforms. New, intercollegiate research published Nov. 27 in Science, co-led by Northeastern University researcher Chenyan Jia, sidesteps this issue by installing an extension on consenting participants’ browsers that automatically reranks the posts those users see, in real time and still... Jia and her team discovered that after one week, users’ feelings toward the opposing party shifted by about two points — an effect normally seen over three years — revealing algorithms’ strong influence on...

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