Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
This work addresses hate speech detection on platforms like Reddit, offering incremental improvements through community-specific modeling and enhanced context analysis.
The paper tackled the problem of predicting harmful discussions on social media by integrating Graph Transformer Networks to analyze conversation context and community-specific factors, resulting in a two-fold performance improvement and a 28% accuracy increase compared to limited context models.
We propose a system to predict harmful discussions on social media platforms. Our solution uses contextual deep language models and proposes the novel idea of integrating state-of-the-art Graph Transformer Networks to analyze all conversations that follow an initial post. This framework also supports adapting to future comments as the conversation unfolds. In addition, we study whether a community-specific analysis of hate speech leads to more effective detection of hateful discussions. We evaluate our approach on 333,487 Reddit discussions from various communities. We find that community-specific modeling improves performance two-fold and that models which capture wider-discussion context improve accuracy by 28\% (35\% for the most hateful content) compared to limited context models.