MM-HSD: Multi-Modal Hate Speech Detection in Videos
| Type of publication: | Conference paper |
| Citation: | Cespedes-Sarrias_ACMMM25_2025 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of the 33rd ACM International Conference on Multimedia (MM'25), October 27-31, 2025, Dublin, Ireland. |
| Year: | 2025 |
| DOI: | https://doi.org/10.1145/3746027.3754558 |
| Abstract: | 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. |
| Main Research Program: | Sustainable & Resilient Societies |
| Keywords: | deep learning, Hate Speech, Multimodal fusion, social media |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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