University of Kwazulu-Natal

Aitopics Reranking Partisan Animosity In Algorithmic Social Media

Bonisiwe Shabane
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aitopics reranking partisan animosity in algorithmic social media

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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.” 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... 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... Assistant Professor of Computer Science, Johns Hopkins University

This research was partially supported by a Hoffman-Yee grant from the Stanford Institute for Human-Centered Artificial Intelligence. Reducing the visibility of polarizing content in social media feeds can measurably lower partisan animosity. To come up with this finding, my colleagues and I developed a method that let us alter the ranking of people’s feeds, previously something only the social media companies could do. Reranking social media feeds to reduce exposure to posts expressing anti-democratic attitudes and partisan animosity affected people’s emotions and their views of people with opposing political views. I’m a computer scientist who studies social computing, artificial intelligence and the web. Because only social media platforms can modify their algorithms, we developed and released an open-source web tool that allowed us to rerank the feeds of consenting participants on X, formerly Twitter, in real time.

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.

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