  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.
