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 \emph{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.
@article{arxiv.2305.16941,
title = {Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media},
author = {Smitha Milli and Micah Carroll and Yike Wang and Sashrika Pandey and Sebastian Zhao and Anca D. Dragan},
journal= {arXiv preprint arXiv:2305.16941},
year = {2024}
}