Redesigning algorithms to intervene on social norm misperceptions during a national election

For the first time in history, civic discourse commonly occurs in digital environments in which algorithms influence exposure to social information^1,2. It is increasingly important to understand whether and how these algorithms affect political discourse^3,4,5. Here we built custom feed-ranking algorithms with full control over their features, and randomly assigned 2,000 participants to use them for 8 weeks (before and after the 2024 US presidential election). We tested whether an engagement-based algorithm (used on major social media platforms^6,7) amplifies intergroup, moralized and emotional (IME) information in ways that skew perceptions of social norms around political dialogue^5,8, and whether it increased engagement with IME content and perceptions of partisan animosity (compared with a reverse-chronological feed^9,10). We also developed and tested a ‘diversified extremity’ algorithm to reduce the influence of extreme users^11,12,13 to improve the accuracy of social norm perception^14,15,16 and reduce perceptions of partisan animosity. We found that engagement-based feeds amplified IME and toxic content relative to reverse-chronological feeds, with the largest increases in moral outrage and political content. Engagement-based feeds also reduced prescriptive norm perception accuracy (albeit in an unexpected direction) and increased perceived partisan animosity. However, they did not significantly alter users’ own engagement behaviours. The diversified extremity algorithm reduced IME and toxic content exposure, improved prescriptive norm accuracy, yet maintained comparable platform enjoyment—suggesting that reducing the influence of extreme users can curb algorithmic distortions without diminishing user experience.

protocol registration The Stage 1 protocol for this Registered Report was accepted in principle on 17 September 2024. The protocol, as accepted by the journal, can be found at https://osf.io/c9a3m.