The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement

2026-03-20

AI Summary

This study investigates whether prosocial content-ranking algorithms can mitigate societal polarization without compromising user engagement on major social media platforms. During the 2024 U.S. presidential election, a six-month randomized controlled trial was conducted via a browser extension involving 9,386 users across Facebook, Reddit, and X/Twitter, deploying five distinct prosocial ranking interventions. This marks the first systematic comparison of algorithmic strategies’ effects on sociopolitical outcomes in real-world, multi-platform environments. Results indicate a statistically significant reduction in affective polarization by an average of 0.03 standard deviations (p<0.05) and a 1.5-point narrowing of partisan affective temperature gaps. Notably, overall user engagement remained stable, with daily time spent on X even increasing by 0.32 minutes. The findings demonstrate that cross-platform algorithmic interventions can effectively attenuate polarization while preserving commercial viability.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Voting

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

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.

Problem

• affective polarization
• social media
• engagement
• prosocial content
• algorithmic ranking

Innovation

• prosocial ranking
• affective polarization
• algorithmic intervention
• social media engagement
• multi-platform experiment