Enhancement of profanity filtering and hate speech detection algorithm applied in Minecraft chats

ABSTRACT: This study addresses critical limitations in an existing profanity-filtering algorithm: insufficient context interpretation, absence of leetspeak detection, and lack of customization capabilities. First, the researchers integrated the BERT transformer model to improve context-sensitive filtering, achieving a 99.1% accuracy rate and a 10.4% increase in correctly censoring 1,000 chat results from the Minecraft-Server-Chat dataset. Toxicity scoring with Toxic-BERT allowed precise filtering, distinguishing between friendly and harmful content words. Second, the researchers incorporated a reverse mapping function to identify leetspeak, significantly improving censorship accuracy. In the dataset of 1,000 chats in Minecraft-Server-Chat dataset, 108 leetspeak inputs were analyzed. The Enhanced Algorithm demonstrates an 82.4% censorship success rate for leetspeak-masked inputs, reducing the error rate to 2.8% compared to the Existing Algorithm’s 10.2%. Furthermore, a customization function was introduced, allowing users to add and remove profane words, ensuring adaptability to shifting language trends and cultural nuances. It was found that the Enhanced algorithm had a performance improvement of 8.3% over the existing algorithm. These advancements make the Enhanced Algorithm a robust, context-aware, accuracy in leetspeaks, and user-centric tool for moderating Minecraft chats, fostering a safer and more inclusive online environment.

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