Social Hatred: Efficient Multimodal Detection of Hatemongers
This addresses the challenge of user-level hate speech detection for online platform moderation, though it appears incremental as it builds on existing multimodal approaches.
The paper tackles the problem of detecting hate-mongering users online by proposing a multimodal aggregative approach that incorporates text, user activity, and network data, showing significant improvement over previous text- and graph-based methods across Twitter, Gab, and Parler datasets.
Automatic detection of online hate speech serves as a crucial step in the detoxification of the online discourse. Moreover, accurate classification can promote a better understanding of the proliferation of hate as a social phenomenon. While most prior work focus on the detection of hateful utterances, we argue that focusing on the user level is as important, albeit challenging. In this paper we consider a multimodal aggregative approach for the detection of hate-mongers, taking into account the potentially hateful texts, user activity, and the user network. Evaluating our method on three unique datasets X (Twitter), Gab, and Parler we show that processing a user's texts in her social context significantly improves the detection of hate mongers, compared to previously used text and graph-based methods. We offer comprehensive set of results obtained in different experimental settings as well as qualitative analysis of illustrative cases. Our method can be used to improve the classification of coded messages, dog-whistling, and racial gas-lighting, as well as to inform intervention measures. Moreover, we demonstrate that our multimodal approach performs well across very different content platforms and over large datasets and networks.