ToxBuster: In-game Chat Toxicity Buster with BERT

• URL: http://arxiv.org/abs/2305.12542v1
• Date: Sun, 21 May 2023 18:53:26 GMT
• Title: ToxBuster: In-game Chat Toxicity Buster with BERT
• Authors: Zachary Yang, Yasmine Maricar, Mohammadreza Davari, Nicolas Grenon-Godbout, Reihaneh Rabbany
• Abstract summary: ToxBuster is a simple and scalable model trained on a relatively large dataset of 194k lines of game chat from Rainbow Six Siege and For Honor. Compared to the existing state-of-the-art, ToxBuster achieves 82.95% (+7) in precision and 83.56% (+57) in recall.
• Score: 2.764897610820181
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Detecting toxicity in online spaces is challenging and an ever more pressing problem given the increase in social media and gaming consumption. We introduce ToxBuster, a simple and scalable model trained on a relatively large dataset of 194k lines of game chat from Rainbow Six Siege and For Honor, carefully annotated for different kinds of toxicity. Compared to the existing state-of-the-art, ToxBuster achieves 82.95% (+7) in precision and 83.56% (+57) in recall. This improvement is obtained by leveraging past chat history and metadata. We also study the implication towards real-time and post-game moderation as well as the model transferability from one game to another.

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