Publication

Abstract : With the emergence of diverse languages, cultural nuances, and code-mixed content, detecting offensive language on social media has become critical. This paper addresses these challenges with the help of the multilingual T5 (mT5) model, which has been fine-tuned to detect offensive language in code-mixed multilingual texts. The system is designed as a browser-based web extension that offers real-time detection by blurring or concealing offensive content, which leads to continuous improvement. It also integrates user-centric features that enable users to provide feedback on the accuracy of offensive content detection and report any instances of offensive language that go undetected, supporting continuous enhancement. This user-centric approach presents an accurate, efficient, and context-aware offensive language detection experience, which leads to safer and more secure online environments for different communities.