ToxBuster: In-game Chat Toxicity Buster with BERT

Autor:	Yang, Zachary, Maricar, Yasmine, Davari, MohammadReza, Grenon-Godbout, Nicolas, Rabbany, Reihaneh
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: 11 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.12542 Zobrazit plný text záznamu View this record from Arxiv