ToxBuster: In-game Chat Toxicity Buster with BERT
This addresses the problem of toxic behavior in online gaming for players and moderators, but it is incremental as it builds on existing methods with specific data and features.
The paper tackled toxicity detection in online game chat by introducing ToxBuster, a model that achieved 82.95% precision and 83.56% recall, improving over existing state-of-the-art by 7 and 57 points respectively.
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.