Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention

• URL: http://arxiv.org/abs/2404.14846v3
• Date: Wed, 05 Mar 2025 14:22:04 GMT
• Title: Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention
• Authors: Benedetta Tessa, Lorenzo Cima, Amaury Trujillo, Marco Avvenuti, Stefano Cresci,
• Abstract summary: Current content moderation follows a reactive, trial-and-error approach.<n>We introduce a proactive, predictive approach that enables moderators to anticipate the impact of their actions before implementation.<n>We study the reactions of 16,540 users to a massive ban of online communities on Reddit.
• Score: 0.6918368994425961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current content moderation follows a reactive, trial-and-error approach, where interventions are applied and their effects are only measured post-hoc. In contrast, we introduce a proactive, predictive approach that enables moderators to anticipate the impact of their actions before implementation. We propose and tackle the new task of predicting user abandonment following a moderation intervention. We study the reactions of 16,540 users to a massive ban of online communities on Reddit, training a set of binary classifiers to identify those users who would abandon the platform after the intervention -- a problem of great practical relevance. We leverage a dataset of 13.8 million posts to compute a large and diverse set of 142 features, which convey information about the activity, toxicity, relations, and writing style of the users. We obtain promising results, with the best-performing model achieving micro F1-score = 0.914. Our model shows robust generalizability when applied to users from previously unseen communities. Furthermore, we identify activity features as the most informative predictors, followed by relational and toxicity features, while writing style features exhibit limited utility. Theoretically, our results demonstrate the feasibility of adopting a predictive machine learning approach to estimate the effects of moderation interventions. Practically, this work marks a fundamental shift from reactive to predictive moderation, equipping platform administrators with intelligent tools to strategically plan interventions, minimize unintended consequences, and optimize user engagement.

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