The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
This addresses the societal issue of polarization on social media for users and platforms, but it is incremental as it builds on existing algorithmic interventions.
The study tackled the problem of reducing affective polarization on social media by testing five alternative ranking algorithms across three platforms during the US 2024 presidential election, resulting in a reduction of polarization by 0.03 standard deviations and mixed effects on user engagement time.
We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify which posts were shown to desktop social media users, randomly assigning 9,386 users to a control group or one of five alternative ranking algorithms which simultaneously altered content across three platforms for six months during the US 2024 presidential election. This reduced our preregistered index of affective polarization by an average of 0.03 standard deviations (p < 0.05), including a 1.5 degree decrease in differences between the 100 point inparty and outparty feeling thermometers. We saw reductions in active use time for Facebook (-0.37 min/day) and Reddit (-0.2 min/day), but an increase of 0.32 min/day (p < 0.01) for X/Twitter. We saw an increase in reports of negative social media experiences but found no effects on well-being, news knowledge, outgroup empathy, perceptions of and support for partisan violence. This implies that bridging content can improve some societal outcomes without necessarily conflicting with the engagement-driven business model of social media.