WiDe-analysis: Enabling One-click Content Moderation Analysis on Wikipedia's Articles for Deletion

TL;DR

WiDe-analysis introduces a comprehensive Python toolkit and dataset for one-click analysis of Wikipedia deletion discussions, aiming to unify and accelerate research in automated content moderation.

Contribution

This work provides the first integrated suite of tools, data, and models for analyzing Wikipedia deletion discussions, promoting reproducibility and further research in content moderation.

Findings

Released a Python package for content moderation analysis

Provided datasets and models for Wikipedia deletion discussions

Facilitated research acceleration with a HuggingFace space

Abstract

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.