University of Washington collaborates on study using AI to reduce political polarization on social media

 By many measures, political polarization in the U.S. has grown in recent years. It’s reflected in recent surveys which show record high numbers of Americans who identify as conservative or liberal, or the stark differences between Republicans’ and Democrats’ current feelings toward the federal government.     Social media can exacerbate this polarization, especially when algorithms social media companies use feed content that not only aligns with a user’s political views but also attacks the opposing party’s candidates or values. But what if you could bypass that algorithm to make posts that expressed partisan animosity or antidemocratic content less prominent?     Martin Saveski is an assistant professor in the University of Washington’s School of Information who recently explored these questions with researchers at Stanford University and Northeastern University. The scientists developed a tool that used AI to quickly scan social media posts that contained antidemocratic views or political animus, such as support for jailing political opponents. Saveski and his team used this tool in a study with Republicans and Democrats that reordered the participants’ feeds on the social media site X so that antidemocratic or politically hostile content appeared higher or lower on their feeds for seven days during last year’s U.S. presidential election.    Saveski joins us to share the study’s results and the implications of giving users greater control over their social media algorithms.