Offensive Language Detection

Offensive language detection aims to automatically identify hateful, abusive, or otherwise harmful language in digital text, fostering safer online environments. Current research emphasizes improving model robustness against adversarial attacks and enhancing generalizability across languages and cultures, often employing transformer-based architectures like BERT and its variants, along with techniques like data augmentation and multi-task learning. This field is crucial for mitigating the negative impacts of online toxicity, informing the development of more ethical and inclusive online platforms, and advancing natural language processing research in low-resource languages.