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Quantifying feature importance for online content moderation
Benedetta Tessa1,2, Alejandro Moreo3, Stefano Cresci2
1Dipartimento di Informatica, Università di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127 Italy.
EPJ Data Science
|August 8, 2026
Summary
Understanding user behavior after moderation is crucial. This study identifies key features predicting changes in online activity and toxicity, but predicts participation diversity less accurately.
Area of Science:
- Computational Social Science
- Online Community Management
- Behavioral Economics
Background:
- Effective online moderation requires predicting user responses to interventions.
- User characteristics significantly influence behavioral changes post-moderation.
- Large-scale data analysis is needed to identify predictive features.
Purpose of the Study:
- To analyze the informativeness of 753 socio-behavioral, linguistic, relational, and psychological features.
- To predict behavioral changes in 16.8K users following a Reddit moderation intervention.
- To identify key features for effective, user-centered moderation strategies.
Main Methods:
- Framed prediction as a quantification task, suitable for aggregate behavior shifts.
- Applied greedy feature selection to identify and rank informative features.
- Analyzed behavioral changes across activity, toxicity, and participation diversity.
Main Results:
- Predictive performance varied by task: activity and toxicity changes were reliably estimated.
- Changes in participation diversity were significantly harder to predict.
- A small subset of features consistently improved performance across tasks.
Conclusions:
- Selected features outperformed single groups and the full feature set, highlighting the importance of complementary signals.
- Post-moderation user behavior is complex and task-dependent.
- Moderation strategies should be tailored to user characteristics and specific behavioral outcomes.
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