CONF Cespedes-Sarrias_ACMMM25_2025/IDIAP MM-HSD: Multi-Modal Hate Speech Detection in Videos Céspedes-Sarrias, Berta Collado-Capell, Carlos Rodenas-Ruiz, Pablo Hrynenko, Olena Cavallaro, Andrea deep learning Hate Speech Multimodal fusion social media EXTERNAL http://publications.idiap.ch/attachments/papers/2025/Cespedes-Sarrias_ACMMM25_2025.pdf PUBLIC Proceedings of the 33rd ACM International Conference on Multimedia (MM'25), October 27-31, 2025, Dublin, Ireland. 2025 https://doi.org/10.1145/3746027.3754558 doi While hate speech detection (HSD) has been extensively studied in text, existing multi-modal approaches remain limited, particularly in videos. As modalities are not always individually informative, simple fusion methods fail to fully capture inter-modal dependencies. Moreover, previous work often omits relevant modalities such as on-screen text and audio, which may contain subtle hateful content and thus provide essential cues, both individually and in combination with others. In this paper, we present MM-HSD, a multi-modal model for HSD in videos that integrates video frames, audio, and text derived from speech transcripts and from frames (i.e. on-screen text) together with features extracted by Cross-Modal Attention (CMA). We are the first to use CMA as an early feature extractor for HSD in videos, to systematically compare query/key configurations, and to evaluate the interactions between different modalities in the CMA block. Our approach leads to improved performance when on-screen text is used as a query and the rest of the modalities serve as a key. Experiments on the HateMM dataset show that MM-HSD outperforms state-of-the-art methods on M-F1 score (0.874), using concatenation of transcript, audio, video, on-screen text, and CMA for feature extraction on raw embeddings of the modalities. The code is available at https://github.com/idiap/mm-hsd.