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。

针对游戏内聊天的实时上下文有害内容检测

Towards Detecting Contextual Real-Time Toxicity for In-Game Chat

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.

介绍了 ToxBuster ，一种简单且可扩展的模型，用于检测在线空间中的毒性。该模型使用彩虹六围攻和荣耀战荣耀的游戏聊天记录数据进行训练，并且对不同类型的毒性进行了仔细的注释。相较于现有技术，ToxBuster 在精确性和召回率上均表现出较大提升，达到了 82.95% 和 83.56%。该模型使用了过去的聊天历史和元数据，并研究了实时和游戏后的监管以及模型从一个游戏传输到另一个游戏的可行性。