Generalizable BERT-Based Cross-Media Sexism Classification

Sexism has pervasive negative effects on both individuals and society. This paper presents a generalizable BERT-based approach to identifying and classifying the source intent of sexism across different social network channels. This approach focuses on individual models trained on the text of tweets and then applied to both Meme (image) and Video data using OCR and annotations respectively. The identification model performed well across all channels and the classification model performed well on both Tweets and Memes. This research suggests that a single model, fine-tuned on one media type can be effectively applied to multiple media types with minimal data preprocessing required.