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arXiv · 2411.14652

Reranking partisan animosity in algorithmic social media feeds alters affective polarization

Tiziano Piccardi·Martin Saveski·Chenyan Jia·Jeffrey T. Hancock·Jeanne L. Tsai·Michael Bernstein
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Abstract

Today, social media platforms hold sole power to study the effects of feed ranking algorithms. We developed a platform-independent method that reranks participants' feeds in real-time and used this method to conduct a preregistered 10-day field experiment with 1,256 participants on X during the 2024 U.S. presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by two points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings.

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Tiziano Piccardi, Martin Saveski, Chenyan Jia, Jeffrey T. Hancock, Jeanne L. Tsai, Michael Bernstein. 2024-11-22. Reranking partisan animosity in algorithmic social media feeds alters affective polarization. https://doi.org/10.1126/science.adu5584

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