Real-Time Toxicity Detection in Games: Balancing Moderation and Player Experience

This post details a real-time toxicity detection system for gaming. It uses Confluent Cloud for Kafka transport and Apache Flink for initial triage with lightweight ML models. High-risk messages are sent to Databricks for deeper NLP analysis. Tableflow is used to convert Kafka streams to Delta tables for Databricks. The system distinguishes between 'OK', 'Toxic', and 'Requires NLP Determination' messages, with a feedback loop for continuous model training using human moderator input. The architecture prioritizes low latency for the majority of chats while ensuring accurate detection of nuanced toxicity.