近日，2025年国际计算语言学会议（COLING 2025）公布了录用论文列表，硕士生于迩晨关于仇恨模因检测的研究成果被录用为长文。国际计算语言学大会（International Conference on Computational Linguistics，COLING），是自然语言处理和计算语言学领域的重要国际学术会议，被CCF推荐为B类会议。

题目：HyperPrompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection（HyperPrompt: 基于超图的多模态仇恨检测提示融合模型）

摘要：Multimodal hate detection aims to identify hate content across multiple modalities for promoting a harmonious online environment. Despite promising progress, three critical challenges, the absence of implicit hateful cues, the cross-modal-induced hate, and 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.

多模态仇恨检测旨在识别跨多种模态的仇恨内容，以促进和谐的网络环境。尽管已经取得了一些有前景的进展，但多模态仇恨检测任务中固有的三个关键挑战，即 “隐含仇恨线索的缺失”、“跨模态引发的仇恨” 以及 “仇恨目标群体的多样性”，一直被忽视。为应对这些挑战，我们提出了一种基于超图的提示融合模型。我们的模型首先使用定制的提示来推断隐含的仇恨线索。然后引入超边来捕捉跨模态引发的仇恨，并应用一种面向多样性的超边扩展策略来考虑不同的仇恨目标群体。最后，通过超图卷积融合各种仇恨线索，加强对跨模态仇恨的探索以及针对特定群体的检测。在两个基准数据集上的实验结果表明，我们的模型在多模态仇恨检测方面达到了最先进的性能。