HyperHatePrompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection (2025.coling-main)
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| Challenge: | Existing models for multimodal hate detection lack implicit hateful cues, cross-modal-induced hate, and diversity of hate target groups. |
| Approach: | They propose a hypergraph-based prompting fusion model that uses LLMs to generate hate cue prompts and hypergraph learning to merge multimodal hate features. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it can detect hate content across multiple modalities. |
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| Challenge: | Existing approaches to multimodal hateful content detection focus on detecting hate speech from text-based content, but they fail to address modality-specific features. |
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| Challenge: | Existing systems struggle with multimodal content where the emergent meaning transcends the aggregation of individual modalities. |
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