University of Kwazulu-Natal

Pdf Social Media Research Tool Can Lower Political Temperature It Coul

Bonisiwe Shabane
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pdf social media research tool can lower political temperature it coul

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... 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.

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...

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. Recent research demonstrates that a web-based tool can reduce partisan hostility on social media feeds by downranking posts with antidemocratic attitudes and partisan animosity—without removing content or requiring platform cooperation. In a 10-day experiment with 1,200 participants, those exposed to less polarizing content reported more positive views of the opposing party, regardless of political affiliation. This approach highlights the potential for users to gain greater control over their social media algorithms, fostering healthier discourse and reducing negative emotions. The tool’s code is available for further development and independent research applications.

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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...

27 In Science. A New Stanford-led Study Is Challenging The

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 ...

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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 t...

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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 t...