Online Sexism

Online sexism research focuses on automatically detecting and classifying sexist language in online spaces, aiming to mitigate its harmful effects. Current research employs various natural language processing (NLP) techniques, including transformer-based models like BERT and RoBERTa, often enhanced by multi-task learning and data augmentation strategies to improve accuracy and address class imbalances in datasets. This work is significant for developing effective content moderation tools and furthering our understanding of how sexism manifests online, contributing to safer digital environments and informing social scientific inquiry into online gender dynamics.