# Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos

arXiv: https://arxiv.org/abs/2602.15757 · HTML: https://mlsanity.com/paper/2602.15757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 63 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Builds on (most cited first, 14 of 14)

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- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- ["Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples](https://mlsanity.com/paper/2004.12764/) (2020)
- [SemEval-2023 Task 10: Explainable Detection of Online Sexism](https://mlsanity.com/paper/2303.04222/) (2023)
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- [HateMM: A Multi-Modal Dataset for Hate Video Classification](https://mlsanity.com/paper/2305.03915/) (2023)
- [MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili](https://mlsanity.com/paper/2408.03468/) (2024)
- [Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language](https://mlsanity.com/paper/2103.10195/) (2021)
- [Shorts vs. Regular Videos on YouTube: A Comparative Analysis of User Engagement and Content Creation Trends](https://mlsanity.com/paper/2403.00454/) (2024)
- [Salamandra Technical Report](https://mlsanity.com/paper/2502.08489/) (2025)
- ["How over is it?" Understanding the Incel Community on YouTube](https://mlsanity.com/paper/2001.08293/) (2020)
- [HateClipSeg: A Segment-Level Annotated Dataset for Fine-Grained Hate Video Detection](https://mlsanity.com/paper/2508.01712/) (2025)
- [MuSeD: A Multimodal Spanish Dataset for Sexism Detection in Social Media Videos](https://mlsanity.com/paper/2504.11169/) (2025)
- [Beyond Binary Moderation: Identifying Fine-Grained Sexist and Misogynistic Behavior on GitHub with Large Language Models](https://mlsanity.com/paper/2507.20358/) (2025)

## Abstract (arXiv)

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.