Social Hatred: Efficient Multimodal Detection of Hatemongers (2025)

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.

## introduction the rising popularity of social platforms coincides with the proliferation of online hate speech and a surge in hateful content targeting minorities @xcite . accordingly, there is a growing body of research on the appearance and magnitude of hate speech on social media @xcite @xcite , and on hate speech detection @xcite @xcite . online hate is often linked to real-world violence @xcite @xcite @xcite . hate is often promoted by communities, rather than by isolated individuals. shifting the focus from the utterance (post) level to the user and community level can be beneficial: it may provide a better understanding of hate group dynamics; it could improve detection of coded language (dog whistling, gas-lighting) at the post level; and it could inform intervention strategies foot_0 @xcite . challenges in utterance-level hate speech classification are well-documented @xcite @xcite , and even state-of-the-art llms show shortcomings in nuanced social tasks like hate detection @xcite . contextual information and aggregation at the user level can help mitigating challenges posed by individual posts. multiple implicit posts from one user, or association with other hateful users/posts, can reinforce weak signals. however, aggregation requires careful consideration of prediction confi-text dhb js ftdb gpt4 gemini 1.5pro 1 "muslim terrorists did this and obama brought them here to run our government. gotta get rid of obammy, omar and all these other f**king muslims" .57 .9 .98 ✓ ✓ 2 "is there a more debased creature in our modern pop-cultural cesspool than (((amy schumer)))? probably not." .22 .35 .92 ✓ ? ✓ 3 "we must secure the existence of our people and a future for white children!" .03 .36 .02 ✓ ✓ ? ## datasets and annotation we use three datasets: twitter-echo, gab, and parler. descriptive statistics are provided in tables 2 and 3 . echo (twitter) the triple parentheses, or triple brackets, also known as the (((echo))), is an antisemitic symbol that is used to highlight the names of individuals of jewish background (e.g., actress and comedian amy schumer, see utterance #2 in ## conclusion in this work, we proposed a robust and efficient multimodal aggregative method, combining text and social context through relational and distributional aggregations. we demonstrated the benefits of this approach for the task of hate speech and hatemonger detection over three unique and very different datasets from three social platforms: x (twitter), gab, and parler, which we curated our-selves as part of this research and for future studies to come. we evaluated our methods on both a large and unique corpus constructed around an ambiguous antisemitic meme and two other datasets based on platforms known for their free speech agenda, and demonstrated their ability to correctly detect hate mongers across different social platforms. we showed how analyzing both the content and the network features of the users significantly improves the ability to detect hate mongers and provides insights on the promotion of hate. our findings underscore the importance of a multi modal approach in tackling the complex issue of online hate speech. by integrating textual analysis with social network dynamics, our method achieves higher accuracy and robustness compared to traditional text-only models. furthermore, our approach is scalable and adaptable to various social media platforms, making it a valuable tool for researchers and practitioners aiming to monitor and mitigate hate speech online. we believe that this method can assist in early detection and intervention strategies, ultimately contributing to safer online communities.