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

TL;DR

This paper introduces a novel approach combining Graph Transformer Networks and community-specific analysis to predict harmful Reddit discussions, significantly improving detection accuracy by leveraging conversation context and community nuances.

Contribution

It presents a new framework integrating Graph Transformer Networks with community-specific modeling for more effective hate speech detection in social media discussions.

Findings

Community-specific models double detection performance.

Wider discussion context improves accuracy by 28%.

Context-aware models perform better on highly hateful content.

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.