Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
This work addresses transparency in content moderation for Wikipedia editors, though it is incremental as it builds on existing stance detection methods.
The authors tackled the problem of non-transparent content moderation by constructing a multilingual dataset of Wikipedia editor discussions and demonstrating that stance and reason can be predicted jointly with high accuracy, adding transparency to the decision-making process.
The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the content moderation policies as explanations for making moderation decisions. Currently, only a few comments explicitly mention those policies -- 20% of the English ones, but as few as 2% of the German and Turkish comments. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages. The dataset contains the stances of the editors (keep, delete, merge, comment), along with the stated reason, and a content moderation policy, for each edit decision. We demonstrate that stance and corresponding reason (policy) can be predicted jointly with a high degree of accuracy, adding transparency to the decision-making process. We release both our joint prediction models and the multilingual content moderation dataset for further research on automated transparent content moderation.