Real-time toxicity detection in online environments poses a significant
challenge, due to the increasing prevalence of social media and gaming
platforms. We introduce ToxBuster, a simple and scalable model that reliably
detects toxic content in real-time for a line of chat by including chat history
and metadata. ToxBuster consistently outperforms conventional toxicity models
across popular multiplayer games, including Rainbow Six Siege, For Honor, and
DOTA 2. We conduct an ablation study to assess the importance of each model
component and explore ToxBuster's transferability across the datasets.
Furthermore, we showcase ToxBuster's efficacy in post-game moderation,
successfully flagging 82.1% of chat-reported players at a precision level of
90.0%. Additionally, we show how an additional 6% of unreported toxic players
can be proactively moderated.

ToxBuster 是一个简单且可伸缩的模型，通过包含聊天历史和元数据，可在实时环境中可靠地检测毒性内容，适用于社交媒体和游戏平台，在流行的多人游戏中性能稳定，并在聊天举报玩家的精确度达到 90% 的情况下成功标记大约 82.1% 的玩家以及主动监管未报道的约 6% 的有毒玩家。