"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. Re-ranking 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."
"Because only social media platforms can modify their algorithms, we developed and released an open-source web tool that allowed us to re-rank the feeds of consenting participants on X, formerly Twitter, in real time. Drawing on social science theory, we used a large language model to identify posts likely to polarize people, such as those advocating political violence or calling for the imprisonment of members of the opposing party."
An open-source web tool re-ranked consenting participants' X feeds in real time to lower the visibility of posts expressing anti-democratic attitudes and partisan animosity. A large language model, informed by social science theory, identified posts likely to polarize, including calls for political violence or imprisonment of opposing-party members. Target posts were not removed but ranked lower, reducing exposure and requiring more scrolling to see them. A ten-day experiment before the 2024 U.S. presidential election measured emotions and attitudes. Reduced exposure decreased negative emotions while scrolling and improved feelings toward political opponents similarly across party affiliations.
Read at Fast Company
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