@inproceedings{xu-etal-2025-hyperhateprompt,
    title = "{H}yper{H}ate{P}rompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection",
    author = "Xu, Bo  and
      Yu, Erchen  and
      Zhou, Jiahui  and
      Lin, Hongfei  and
      Zong, Linlin",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.258/",
    pages = "3825--3835",
    abstract = "Multimodal hate detection aims to identify hate content across multiple modalities for promoting a harmonious online environment. Despite promising progress, three critical challenges, \textit{the absence of implicit hateful cues}, \textit{the cross-modal-induced hate}, and \textit{the diversity of hate target groups}, inherent in the multimodal hate detection task, have been overlooked. To address these challenges, we propose a hypergraph-based prompting fusion model. Our model first uses tailored prompts to infer implicit hateful cues. It then introduces hyperedges to capture cross-modal-induced hate and applies a diversity-oriented hyperedge expansion strategy to account for different hate target groups. Finally, hypergraph convolution fuses diverse hateful cues, enhancing the exploration of cross-modal hate and targeting specific groups. Experimental results on two benchmark datasets show that our model achieves state-of-the-art performance in multimodal hate detection."
}