ToxBuster: In-game Chat Toxicity Buster with BERT

Zachary Yang, Yasmine Maricar, MohammadReza Davari, Nicolas Grenon-Godbout, Reihaneh Rabbany
5/21/2023
cs.CLcs.CY

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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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Cite this paper

@article{yang2023toxbuster,
  title  = {ToxBuster: In-game Chat Toxicity Buster with BERT},
  author = {Zachary Yang and Yasmine Maricar and MohammadReza Davari and Nicolas Grenon-Godbout and Reihaneh Rabbany},
  year   = {2023},
  eprint = {2305.12542},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.12542v1}
}

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