Engagement, user satisfaction, and the amplification of divisive content on social media

Abstract Social media ranking algorithms typically optimize for users’ revealed preferences, i.e. user engagement such as clicks, shares, and likes. Many have hypothesized that by focusing on users’ revealed preferences, these algorithms may exacerbate human behavioral biases. In a preregistered 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…

• NS
  National Science Foundation
• UO
  University of California Berkeley