  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.
