Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
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.
Cite
@article{arxiv.2301.04248,
title = {Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context},
author = {Liam Hebert and Lukasz Golab and Robin Cohen},
journal= {arXiv preprint arXiv:2301.04248},
year = {2023}
}
Comments
Accepted and Presented at WI-IAT 22