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[2602.15757] Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos
Summary
The paper presents FineMuSe, a new dataset for detecting nuanced sexism in social media videos, addressing the limitations of binary classification in automated tools.
Why It Matters
As online sexism manifests in various subtle forms, this research is crucial for developing more effective automated detection systems. By introducing a multimodal dataset and a hierarchical taxonomy, it enhances the understanding and identification of nuanced sexist content, which is essential for fostering safer online environments.
Key Takeaways
- FineMuSe dataset includes both binary and fine-grained sexism annotations.
- A comprehensive taxonomy categorizes various forms of sexism and rhetorical devices.
- Multimodal LLMs show competitive performance with human annotators but struggle with visual cues.
- The research highlights the need for context-sensitive labels in automated sexism detection.
- Improving detection tools can contribute to reducing online sexism.
Computer Science > Computation and Language arXiv:2602.15757 (cs) [Submitted on 17 Feb 2026] Title:Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos Authors:Laura De Grazia, Danae Sánchez Villegas, Desmond Elliott, Mireia Farrús, Mariona Taulé View a PDF of the paper titled Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos, by Laura De Grazia and 4 other authors View PDF HTML (experimental) Abstract: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. Subjects: Computation and La...
