Sexism Identification Using Annotator Ranking in Memes: A Multimodal Approach Using Transformers

Abstract

Memes are a popular medium for sharing information on social media, often embedding humor and interactive content. However, they can also propagate sexism, targeting specific genders, particularly females. This paper presents a multimodal approach to detect sexism in memes and classify the intent of sexist memes and sexism categorization. We leverage BERT for textual analysis, BLIP for multimodal processing, and Vision Transformers (ViT) for image feature extraction. Our model achieves approximately 68.49% accuracy in identifying sexist memes and 68.52% accuracy in determining the source intention and 49.31% accuracy in Sexism Categorization. This work contributes to creating safer digital spaces by automating the detection of biased content on social media. © 2025 Copyright for this paper by its authors.