  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.
