Social Hatred: Efficient Multimodal Detection of Hatemongers

TL;DR

This paper presents a multimodal approach for detecting online hate-mongers by analyzing texts, user activity, and networks, significantly improving detection accuracy across multiple social media platforms.

Contribution

It introduces a novel user-level multimodal method that combines text, activity, and network data for hate speech detection, outperforming previous text and graph-based techniques.

Findings

Processing social context enhances hate-monger detection accuracy.

Method performs well across diverse platforms like Twitter, Gab, and Parler.

Results show significant improvement over prior unimodal approaches.

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. 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.