Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context

• URL: http://arxiv.org/abs/2301.04248v1
• Date: Tue, 10 Jan 2023 23:47:13 GMT
• Title: Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
• Authors: Liam Hebert, Lukasz Golab, Robin Cohen
• Abstract summary: We propose a system to predict harmful discussions on social media platforms. Our solution uses contextual deep language models and integrates state-of-the-art Graph Transformer Networks. We evaluate our approach on 333,487 Reddit discussions from various communities.
• Score: 9.4337569682766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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