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 $\approx$ 11 hours of videos extracted from TikTok and BitChute; (2) we propose an innovative annotation framework for analyzing the contribution of textual and multimodal labels in the classification of sexist and non-sexist content; 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.

本研究解决了社交媒体视频中性别歧视内容检测的挑战，提出了一个新的多模态西班牙语数据集MuSeD，包含约11小时的TikTok和BitChute视频。研究创新性地提出了一种注释框架，并评估了多种大型语言模型在性别歧视检测任务中的表现。研究发现视觉信息在标注性别歧视内容中起着关键作用，但模型在隐性歧视的识别上表现不佳，突显了这一任务的复杂性。

MuSeD：用于社交媒体视频中性别歧视检测的多模态西班牙语数据集