MexHat: A Dataset for Hate Speech Detection in Mexican Spanish Videos

Abstract:Ensuring online safety through content monitoring had raised Hate Speech Detection as a crucial task to be addressed. By essence the task demands the capture of contextual cues, which are essential for a precise understanding of the content's intent. Although automated detection approaches for the task have advanced significantly, the scarcity of non-English resources persists, limiting the ability of models to adapt to the subtle, context-dependent, and culturally related nature of multimodal content. In this paper, we introduce MexHat, a video dataset designed to capture the linguistic and cultural cues for the hate-speech detection task in a Mexican Spanish context. Our dataset comprises around 1k video clips annotated across two tasks: a three-way class evaluation (no negative content, offensive content and hate-speech content), and a fine-grained class evaluation including three hate-speech sub-categories. The dataset statistics and the baseline results highlight the inherent challenges associated with the task. Disclaimer: This paper contains sensitive content that may be disturbing to some readers.

Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.31553 [cs.CL]

(or arXiv:2609.31553v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.31553 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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