One of Many: Assessing User-level Effects of Moderation Interventions on r/The_Donald
arXiv:2209.08809 · doi:10.1145/3578503.3583626
Abstract
Evaluating the effects of moderation interventions is a task of paramount importance, as it allows assessing the success of content moderation processes. So far, intervention effects have been almost solely evaluated at the aggregated platform or community levels. Here, we carry out a multidimensional evaluation of the user-level effects of the sequence of moderation interventions that targeted r/The_Donald: a community of Donald Trump adherents on Reddit. We demonstrate that the interventions: 1) strongly reduced user activity; 2) slightly increased the diversity of the subreddits in which users participated; 3) slightly reduced user toxicity; and 4) gave way to the sharing of less factual and more politically biased news. Importantly, we also find that interventions having strong community level effects are associated to extreme and diversified user-level reactions. Our results highlight that community-level effects are not always representative of the underlying behavior of individuals or smaller user groups. We conclude by discussing the practical and ethical implications of our results. Overall, our findings can inform the development of targeted moderation interventions and provide useful guidance for policing online platforms.
Accepted at ACM WebSci'23
References in corpus (4)
- Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels
- SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
- Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald
- Personalized Interventions for Online Moderation
Cited by in corpus (4)
- The Great Ban: Efficacy and Unintended Consequences of a Massive Deplatforming Operation on Reddit
- The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Media
- Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation
- Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention