Quantifying Feature Importance for Online Content Moderation

• URL: http://arxiv.org/abs/2510.19882v1
• Date: Wed, 22 Oct 2025 14:02:30 GMT
• Title: Quantifying Feature Importance for Online Content Moderation
• Authors: Benedetta Tessa, Alejandro Moreo, Stefano Cresci, Tiziano Fagni, Fabrizio Sebastiani,
• Abstract summary: We investigate the informativeness of socio-behavioural, linguistic, relational, and psychological features, in predicting behavioural changes of 16.8K users affected by a major moderation intervention on Reddit.<n>Our results pave the way for the development of accurate systems that predict user reactions to moderation interventions.
• Score: 38.70422886875624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately estimating how users respond to moderation interventions is paramount for developing effective and user-centred moderation strategies. However, this requires a clear understanding of which user characteristics are associated with different behavioural responses, which is the goal of this work. We investigate the informativeness of 753 socio-behavioural, linguistic, relational, and psychological features, in predicting the behavioural changes of 16.8K users affected by a major moderation intervention on Reddit. To reach this goal, we frame the problem in terms of "quantification", a task well-suited to estimating shifts in aggregate user behaviour. We then apply a greedy feature selection strategy with the double goal of (i) identifying the features that are most predictive of changes in user activity, toxicity, and participation diversity, and (ii) estimating their importance. Our results allow identifying a small set of features that are consistently informative across all tasks, and determining that many others are either task-specific or of limited utility altogether. We also find that predictive performance varies according to the task, with changes in activity and toxicity being easier to estimate than changes in diversity. Overall, our results pave the way for the development of accurate systems that predict user reactions to moderation interventions. Furthermore, our findings highlight the complexity of post-moderation user behaviour, and indicate that effective moderation should be tailored not only to user traits but also to the specific objective of the intervention.

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