Offensive Content

Offensive content detection aims to automatically identify harmful language online, including hate speech and cyberbullying, to foster safer digital environments. Current research focuses on improving the generalizability and robustness of detection models, often employing deep learning architectures like transformers (e.g., BERT, its variants, and other LLMs) and exploring techniques like federated learning for privacy preservation. This field is crucial for mitigating the harmful effects of online abuse and is driving advancements in natural language processing, particularly in handling diverse languages and addressing the complexities of implicit or context-dependent offensiveness.