Multimodal Hate Detection Using Dual-Stream Graph Neural Networks


Jiangbei Yue (University of Exeter), Shuonan Yang (University of Exeter), Tailin Chen (University of Exeter), Jianbo Jiao (University of Birmingham), Zeyu Fu (University of Exeter)
The 35th British Machine Vision Conference
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Abstract

Hateful videos present serious risks to online safety and real-world well-being, necessitating effective detection methods. Although multimodal classification approaches integrating information from several modalities outperform unimodal ones, they typically neglect that even minimal hateful content defines a video's category. Specifically, they generally treat all content uniformly, instead of emphasizing the hateful components. Additionally, existing multimodal methods cannot systematically capture essential structured information in videos, which limits the effectiveness of multimodal fusion. To address these limitations, we propose a novel classification model, the multimodal dual-stream graph neural networks. It constructs an instance graph by separating the given video into several instances to extract instance-level features. Then, a complementary weight graph assigns importance weights to these features, highlighting hateful instances. Importance weights and instance features are combined to generate video labels. Our model employs a graph-based framework to systematically model structured relationships within and across modalities. Extensive experiments on public datasets show that our model is state-of-the-art in hateful video classification and has strong explainability.

Citation

@inproceedings{Yue_2025_BMVC,
author    = {Jiangbei Yue and Shuonan Yang and Tailin Chen and Jianbo Jiao and Zeyu Fu},
title     = {Multimodal Hate Detection Using Dual-Stream Graph Neural Networks},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_408/paper.pdf}
}


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