Enhancement of Profanity Filtering and Hate Speech Detection Algorithm applied in Minecraft Chats
Jeffrey M. Daquigan, Gorel Kaiser G. Marbella, Raymund M. Dioses, Joseph Darwin C. Co, Criselle Jose Centeno, Khatalyn E. Mata · Technoarete Transactions on Advances in Computer Applications · 2025
This study addresses critical limitations in an existing profanity-filtering algorithm: insufficient context interpretation and absence of leetspeak detection. 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 28.7%. This gives the enhanced algorithm the accuracy rate of 97.2% in filtering leetspeak-masked profanity compared to the 71.3% of the existing algorithm. These advancements made the Enhanced Algorithm a robust, context-aware and accurate in leetspeaks for moderating Minecraft chats, fostering a safer and more inclusive online environment.