IDO: Incongruity-aware Distribution Optimization for Multimodal Fake News Detection

• URL: http://arxiv.org/abs/2606.03418v1
• Date: Tue, 02 Jun 2026 10:02:52 GMT
• Title: IDO: Incongruity-aware Distribution Optimization for Multimodal Fake News Detection
• Abstract summary: We propose Incongruity-aware Distribution Optimization (IDO) to improve the performance of fake news detection.<n>For factual incongruity, we introduce a channel-wise reweighting strategy to obtain semantically discriminative embeddings.<n>For modality incongruity, we utilize incongruity contrastive learning to learn cross-modal semantic information.
• Score: 6.684896724842189
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal fake news detection aims to identify the authenticity of news. Existing multimodal fake news detection methods mainly focus on cross-modal consistency, but often fail to explicitly model the semantic incongruity that characterizes deceptive multimodal content. However, misinformation often contains semantic information incongruity with the facts. To address these challenges, we propose Incongruity-aware Distribution Optimization (IDO) to improve the performance of fake news detection from the perspectives of factual incongruity and modality incongruity. For factual incongruity, we introduce a channel-wise reweighting strategy to obtain semantically discriminative embeddings and utilize gaussian distribution to model the uncertain correlation caused by factual incongruity. For modality incongruity, we utilize incongruity contrastive learning to learn cross-modal semantic information. Experiments demonstrate that IDO achieves state-of-the-art performance.

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