Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities

ACL ARR 2025 May Submission4895 Authors

Abstract:

Effective content moderation systems require explicit classification criteria, yet online communities like subreddits often operate with diverse, implicit standards. This work introduces a novel approach to identify and extract these implicit criteria from historical moderation data using an interpretable architecture.
We represent moderation criteria as score tables of lexical expressions associated with content removal, enabling systematic comparison across different communities.
Our experiments demonstrate that these extracted lexical patterns effectively replicate the performance of neural moderation models while providing transparent insights into decision-making processes. The resulting criteria matrix reveals significant variations in how seemingly shared norms are actually enforced, uncovering previously undocumented moderation patterns including community-specific tolerances for language, features for topical restrictions, and underlying subcategories of the toxic speech classification.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: content moderation, community-based NLP, data shortcuts/artifacts,reddit,toxicity

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data analysis

Languages Studied: English

Submission Number: 4895