Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media

• URL: http://arxiv.org/abs/2305.16941v5
• Date: Fri, 22 Dec 2023 20:40:35 GMT
• Title: Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media
• Authors: Smitha Milli, Micah Carroll, Yike Wang, Sashrika Pandey, Sebastian Zhao, Anca D. Dragan
• Abstract summary: We find that Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-group hostile content that users say makes them feel worse about their political out-group. We find that users do not prefer the political tweets selected by the algorithm, suggesting that the engagement-based algorithm underperforms in satisfying users' stated preferences.
• Score: 23.3201470123544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a pre-registered randomized experiment, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-group hostile content that users say makes them feel worse about their political out-group. Furthermore, we find that users do not prefer the political tweets selected by the algorithm, suggesting that the engagement-based algorithm underperforms in satisfying users' stated preferences. Finally, we explore the implications of an alternative approach that ranks content based on users' stated preferences and find a reduction in angry, partisan, and out-group hostile content but also a potential reinforcement of echo chambers. The evidence underscores the necessity for a more nuanced approach to content ranking that balances engagement, users' stated preferences, and sociopolitical outcomes.

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