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

2023/05/26 by Smitha Milli, Milli, Smitha, Micah Carroll +9 · 11 voices · 16 citations
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paper · pdf · doi:10.48550/arxiv.2305.16941

Abstract

In a pre-registered algorithmic audit, 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 pro-attitudinal content. The evidence underscores the necessity for a more nuanced approach to content ranking that balances engagement and users' stated preferences.

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