# MuSeD: A Multimodal Spanish Dataset for Sexism Detection in Social Media Videos

arXiv: https://arxiv.org/abs/2504.11169 · HTML: https://mlsanity.com/paper/2504.11169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 12 of 12)

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- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- ["Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples](https://mlsanity.com/paper/2004.12764/) (2020)
- [HateMM: A Multi-Modal Dataset for Hate Video Classification](https://mlsanity.com/paper/2305.03915/) (2023)
- [Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models](https://mlsanity.com/paper/2401.16727/) (2024)
- [Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments](https://mlsanity.com/paper/2109.00227/) (2021)
- [Combining Humor and Sarcasm for Improving Political Parody Detection](https://mlsanity.com/paper/2205.03313/) (2022)
- [ImageChain: Advancing Sequential Image-to-Text Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2502.19409/) (2025)
- [Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks](https://mlsanity.com/paper/2309.07794/) (2023)

## Built on by (most cited first, 2 of 2)

- [Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos](https://mlsanity.com/paper/2602.15757/) (2026)
- [Multimodal Sexism Identification and Characterization using Large Language Models and Gradient Boosting](https://mlsanity.com/paper/2606.05997/) (2026)

## Abstract (arXiv)

Sexism is generally defined as prejudice and discrimination based on sex or gender, affecting every sector of society, from social institutions to relationships and individual behavior. Social media platforms amplify the impact of sexism by conveying discriminatory content not only through text but also across multiple modalities, highlighting the critical need for a multimodal approach to the analysis of sexism online. With the rise of social media platforms where users share short videos, sexism is increasingly spreading through video content. Automatically detecting sexism in videos is a challenging task, as it requires analyzing the combination of verbal, audio, and visual elements to identify sexist content. In this study, (1) we introduce MuSeD, a new Multimodal Spanish dataset for Sexism Detection consisting of ≈ 11 hours of videos extracted from TikTok and BitChute; (2) we propose an innovative annotation framework for analyzing the contributions of textual, vocal, and visual modalities to the classification of content as either sexist or non-sexist; and (3) we evaluate a range of large language models (LLMs) and multimodal LLMs on the task of sexism detection. We find that visual information plays a key role in labeling sexist content for both humans and models. Models effectively detect explicit sexism; however, they struggle with implicit cases, such as stereotypes, instances where annotators also show low agreement. This highlights the inherent difficulty of the task, as identifying implicit sexism depends on the social and cultural context.