AggregHate: An Efficient Aggregative Approach for the Detection of Hatemongers on Social Platforms

TL;DR

This paper introduces AggregHate, an efficient multimodal aggregative method for detecting hate-mongers on social platforms by analyzing texts, user activity, and network context, outperforming previous approaches.

Contribution

The paper presents a novel multimodal aggregative approach that improves hate-monger detection by integrating textual, activity, and network data at the user level.

Findings

Processing user texts in social context enhances detection accuracy.

The method outperforms previous text and graph-based approaches.

Approach is scalable to large datasets and networks.

Abstract

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. We evaluate our methods on three unique datasets X (Twitter), Gab, and Parler showing that a 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. Our method can be then used to improve the classification of coded messages, dog-whistling, and racial gas-lighting, as well as inform intervention measures. Moreover, our approach is highly efficient even for very large datasets and networks.