Sexism Detection

Sexism detection research aims to automatically identify sexist language in online text, focusing on improving accuracy and explainability of detection systems. Current efforts utilize various deep learning architectures, including transformers (like BERT) and convolutional neural networks, often incorporating techniques like transfer learning, multi-task learning, and data augmentation to address challenges such as class imbalance and limited labeled data. This field is crucial for mitigating online harassment and hate speech, with applications ranging from content moderation to understanding societal biases reflected in language.