WiDe-analysis: Enabling One-click Content Moderation Analysis on Wikipedia's Articles for Deletion
This work addresses the problem of fragmented and non-standardized research for researchers and practitioners in NLP and content moderation, though it is incremental as it builds on existing methods without introducing new paradigms.
The paper tackles the scattered and inconsistent research on content moderation in Wikipedia's deletion discussions by introducing WiDe-analysis, a Python package that provides one-click analysis tools, and releases associated data, models, and a HuggingFace space to accelerate automation efforts.
Content moderation in online platforms is crucial for ensuring activity therein adheres to existing policies, especially as these platforms grow. NLP research in this area has typically focused on automating some part of it given that it is not feasible to monitor all active discussions effectively. Past works have focused on revealing deletion patterns with like sentiment analysis, or on developing platform-specific models such as Wikipedia policy or stance detectors. Unsurprisingly, however, this valuable body of work is rather scattered, with little to no agreement with regards to e.g., the deletion discussions corpora used for training or the number of stance labels. Moreover, while efforts have been made to connect stance with rationales (e.g., to ground a deletion decision on the relevant policy), there is little explanability work beyond that. In this paper, we introduce a suite of experiments on Wikipedia deletion discussions and wide-analyis (Wikipedia Deletion Analysis), a Python package aimed at providing one click analysis to content moderation discussions. We release all assets associated with wide-analysis, including data, models and the Python package, and a HuggingFace space with the goal to accelerate research on automating content moderation in Wikipedia and beyond.