University of Kwazulu-Natal

Pdf Reranking Partisan Animosity In Algorithmic Social Media Feeds Alt

Bonisiwe Shabane
-
pdf reranking partisan animosity in algorithmic social media feeds alt

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

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...

Reranking partisan animosity in algorithmic social media feeds alters affective polarization Tiziano Piccardi*, Martin Saveski*, Chenyan Jia*, Jeffery T. Hancock, Jeanne L. Tsai, Michael S. Bernstein Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms.

We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by more than 2 points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content... This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings. Embedding Democratic Values into Social Media AIs via Societal Objective Functions In Proceedings of the ACM: Human-Computer Interaction (CSCW 2024)

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.

People Also Search

ArXivLabs Is A Framework That Allows Collaborators To Develop And

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add v...

New Research Shows The Impact That Social Media Algorithms Can

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...

Reranking Partisan Animosity In Algorithmic Social Media Feeds Alters Affective

Reranking partisan animosity in algorithmic social media feeds alters affective polarization Tiziano Piccardi*, Martin Saveski*, Chenyan Jia*, Jeffery T. Hancock, Jeanne L. Tsai, Michael S. Bernstein Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms.

We Developed A Platform-independent Method That Reranks Participants’ Feeds In

We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure ...

Assistant Professor Of Computer Science, Johns Hopkins University This Research

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 r...