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        <title>Towards Detecting Contextual Real-Time Toxicity for In-Game Chat</title>
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    <abstract>Real-time toxicity detection in online environments poses a significant challenge, due to the increasing prevalence of social media and gaming platforms. We introduce ToxBuster, a simple and scalable model that reliably detects toxic content in real-time for a line of chat by including chat history and metadata. ToxBuster consistently outperforms conventional toxicity models across popular multiplayer games, including Rainbow Six Siege, For Honor, and DOTA 2. We conduct an ablation study to assess the importance of each model component and explore ToxBuster’s transferability across the datasets. Furthermore, we showcase ToxBuster’s efficacy in post-game moderation, successfully flagging 82.1% of chat-reported players at a precision level of 90.0%. Additionally, we show how an additional 6% of unreported toxic players can be proactively moderated.</abstract>
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%0 Conference Proceedings
%T Towards Detecting Contextual Real-Time Toxicity for In-Game Chat
%A Yang, Zachary
%A Grenon-Godbout, Nicolas
%A Rabbany, Reihaneh
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yang-etal-2023-towards-detecting
%X Real-time toxicity detection in online environments poses a significant challenge, due to the increasing prevalence of social media and gaming platforms. We introduce ToxBuster, a simple and scalable model that reliably detects toxic content in real-time for a line of chat by including chat history and metadata. ToxBuster consistently outperforms conventional toxicity models across popular multiplayer games, including Rainbow Six Siege, For Honor, and DOTA 2. We conduct an ablation study to assess the importance of each model component and explore ToxBuster’s transferability across the datasets. Furthermore, we showcase ToxBuster’s efficacy in post-game moderation, successfully flagging 82.1% of chat-reported players at a precision level of 90.0%. Additionally, we show how an additional 6% of unreported toxic players can be proactively moderated.
%R 10.18653/v1/2023.findings-emnlp.663
%U https://aclanthology.org/2023.findings-emnlp.663
%U https://doi.org/10.18653/v1/2023.findings-emnlp.663
%P 9894-9906

Markdown (Informal)

[Towards Detecting Contextual Real-Time Toxicity for In-Game Chat](https://aclanthology.org/2023.findings-emnlp.663) (Yang et al., Findings 2023)

• Towards Detecting Contextual Real-Time Toxicity for In-Game Chat (Yang et al., Findings 2023)

ACL

• Zachary Yang, Nicolas Grenon-Godbout, and Reihaneh Rabbany. 2023. Towards Detecting Contextual Real-Time Toxicity for In-Game Chat. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9894–9906, Singapore. Association for Computational Linguistics.