We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional comment-only methods, our approach to labelling a comment as hate speech involves a holistic analysis of text and images grounded in the discussion context. This is done by leveraging graph transformers to capture the contextual relationships in the discussion surrounding a comment and grounding the interwoven fusion layers that combine text and image embeddings instead of processing modalities separately. To evaluate our work, we present a new dataset, HatefulDiscussions, comprising complete multi-modal discussions from multiple online communities on Reddit. We compare the performance of our model to baselines that only process individual comments and conduct extensive ablation studies.

Internet trolls, by definition, are disruptive, combative and often unpleasant with their offensive or provocative online posts designed to disturb and upset. The common assumption is that people who troll are different from the rest of us, allowing us to dismiss them and their behavior. But research from Stanford University and Cornell University, published as part of the upcoming 2017 Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017), suggests otherwise. The research offers evidence that, under the right circumstances, anyone can become a troll. "We wanted to understand why trolling is so prevalent today," said Justin Cheng, a computer science researcher at Stanford and lead author of the paper.