Uncertainty-aware disentangled representation learning for multimodal fake news detection

Abstract

The proliferation of fake news on social media platforms poses serious societal risks, such as eroding public trust, inciting panic, and influencing policy decisions. While automated multimodal fake news detection has emerged as a promising approach, existing methods face three critical limitations: (1) they often fail to capture uncertainty within multimodal data, (2) they struggle with modality heterogeneity, and (3) they lack a balanced focus on both modality-private veracity and cross-modal inconsistencies. In this work, we propose a novel Uncertainty-aware Disentangled Representation Learning (UDRL) framework that addresses these limitations in three key ways. First, our probabilistic representation module models multimodal information as Gaussian distributions, effectively capturing uncertainty and ambiguity. Second, we introduce a disentangled representation learning framework that separates shared and private modality information, enhancing robustness and discrimination. Finally, our uncertainty-aware fusion module dynamically adjusts modality importance based on uncertainty, facilitating more accurate cross-modal interactions. Experimental results on three benchmark datasets demonstrate that UDRL achieves competitive and consistent performance across datasets, validating its effectiveness in multimodal fake news detection.