Quantifying Feature Importance for Online Content Moderation

TL;DR

This study assesses the informativeness of various user features to predict behavioural responses to moderation on Reddit, aiming to improve targeted moderation strategies and understand post-moderation user behaviour.

Contribution

It introduces a feature selection approach for quantifying the importance of diverse user features in predicting behavioural changes after moderation.

Findings

A small set of features are consistently predictive across tasks.

Predictive accuracy varies by task, with activity and toxicity being easier to estimate.

Many features are task-specific or of limited utility.

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.