Papers › Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
Liam Hebert, Lukasz Golab, Robin Cohen
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.
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