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

2026-06-02

AI Summary

Existing approaches struggle to explicitly model the dual inconsistency between factual content and modality-specific semantics in multimodal fake news. This work proposes the first inconsistency-aware distribution optimization framework, which extracts discriminative semantic embeddings through channel re-weighting, models factual uncertainty using Gaussian distributions, and introduces an inconsistency-aware contrastive learning mechanism to capture cross-modal semantic discrepancies. The proposed method significantly outperforms current state-of-the-art techniques across multiple benchmark datasets, achieving the best-reported performance in multimodal fake news detection.

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.

Problem

• multimodal fake news detection
• semantic incongruity
• factual incongruity
• modality incongruity
• cross-modal consistency

Innovation

• Incongruity-aware
• Distribution Optimization
• Multimodal Fake News Detection
• Contrastive Learning
• Factual Incongruity