Reranking partisan animosity in algorithmic social media feeds alters affective polarization
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866918219950850048 |
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| author | Piccardi, Tiziano Saveski, Martin Jia, Chenyan Hancock, Jeffrey T. Tsai, Jeanne L. Bernstein, Michael |
| author_facet | Piccardi, Tiziano Saveski, Martin Jia, Chenyan Hancock, Jeffrey T. Tsai, Jeanne L. Bernstein, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14652 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reranking partisan animosity in algorithmic social media feeds alters affective polarization Piccardi, Tiziano Saveski, Martin Jia, Chenyan Hancock, Jeffrey T. Tsai, Jeanne L. Bernstein, Michael Computers and Society Artificial Intelligence Human-Computer Interaction Social and Information Networks 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. |
| title | Reranking partisan animosity in algorithmic social media feeds alters affective polarization |
| topic | Computers and Society Artificial Intelligence Human-Computer Interaction Social and Information Networks |
| url | https://arxiv.org/abs/2411.14652 |