Investigando a Dinâmica da Propagação do Ódio em Cascatas de Toxicidade nos Chats Ao Vivo da Twitch

• João Vitor Chagas Lobo UFV
• Daniel Mendes Barbosa UFV
• Philipe de Freitas Melo UFV

DOI: https://doi.org/10.5753/webmedia.2025.15110

Resumo

The popularization of live streaming on platforms such as Twitch has transformed how users interact in real time, creating dynamic environments that are also susceptible to the spread of toxic discourse. This poses a new challenge for the automated moderation of these online communities, especially in the face of coordinated hate actions. This study investigates how toxicity spreads on Twitch through the identification and analysis of cascades of toxic messages. To this end, we developed an automated data collection system that monitored, in real time, messages from streamings of popular Twitch channels. These messages were processed and classified using natural language processing tools, including sentiment analysis and hate speech detection via lexical dictionaries from Hatebase and WeaponizedWord, as well as deep learning models from PerspectiveAPI and pysentimiento. Based on this classification, offensive content in chats was identified, and 472 toxicity cascades were mapped, enabling the analysis of their frequency, intensity, and dissemination patterns. Our results show that although most hate speech is sporadic, some channels exhibit recurrent and intense episodes of collective toxicity. This study contributes to a better understanding of the dynamics of hate speech propagation in live environments and provides insights for future moderation and intervention strategies.

Palavras-chave: discurso de ódio, toxicidade, Twitch, transmissão ao vivo, cascatas de informação, hate speech, live streamings

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