Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos
This addresses the challenge of identifying subtle sexism in online content, which is incremental as it builds on existing binary classification methods.
The paper tackled the problem of detecting fine-grained sexism in social media videos by creating a new multimodal dataset and evaluating LLMs, finding that multimodal LLMs perform competitively with humans but struggle with visual cues for co-occurring types.
Online sexism appears in various forms, which makes its detection challenging. Although automated tools can enhance the identification of sexist content, they are often restricted to binary classification. Consequently, more subtle manifestations of sexism may remain undetected due to the lack of fine-grained, context-sensitive labels. To address this issue, we make the following contributions: (1) we present FineMuSe, a new multimodal sexism detection dataset in Spanish that includes both binary and fine-grained annotations; (2) we introduce a comprehensive hierarchical taxonomy that encompasses forms of sexism, non-sexism, and rhetorical devices of irony and humor; and (3) we evaluate a wide range of LLMs for both binary and fine-grained sexism detection. Our findings indicate that multimodal LLMs perform competitively with human annotators in identifying nuanced forms of sexism; however, they struggle to capture co-occurring sexist types when these are conveyed through visual cues.