Towards Detecting Contextual Real-Time Toxicity for In-Game Chat
This addresses toxicity moderation for online gaming platforms, offering an incremental improvement with practical deployment potential.
The paper tackles real-time toxicity detection in online gaming chat by introducing ToxBuster, a model that uses chat history and metadata, and it outperforms conventional models across games like Rainbow Six Siege, achieving 82.1% recall and 90.0% precision in flagging reported players.
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.