Offensive Content Detection

Offensive content detection aims to automatically identify harmful language, including hate speech and other forms of abuse, in online text and multimedia. Current research focuses on improving the accuracy and generalizability of detection models, often employing transformer-based architectures like BERT and RoBERTa, along with techniques to address data imbalance and cross-lingual challenges. This field is crucial for mitigating the harmful effects of online abuse and fostering safer digital environments, driving advancements in natural language processing and impacting the design of content moderation systems.