A Multimodal Fake News Detection Method Based on Contrastive Learning and Variational Autoencoder

Fake news often exhibits pronounced bias and misleading content. To foster a harmonious information environment, there is an urgent need for rapid fake news identification. Fake news detection can assess news authenticity by analyzing multidimensional information such as text, images, and comments. This automated approach significantly reduces human and material resource costs. However, existing detection methods often focus on extracting textual features, employing coarse-grained fusion techniques when integrating multi-modal information, and neglecting the inherent correlations between different modalities. Meanwhile, these methods rely on static network structures and fixed feature weighting strategies, lacking targeted neural network optimization and adaptive learning mechanisms, which results in insufficient interpretability and limited generalization performance across most detection approaches. To address these challenges, from the perspective of neural network optimization and regularization enhancement, this paper proposes a multi-modal fake news detection method based on contrastive learning and variational autoencoders. Firstly, we design a dual-contrastive learning loss function as a specialized regularization strategy for multimodal neural networks. By learning features through comparing similar and dissimilar samples, it more effectively captures correlations across multimodal data, optimizing the feature distribution and enhancing the model’s generalization capability via contrastive regularization. Second, it introduces a variational autoencoder to realize adaptive learning and dynamic weight optimization assigned to unimodal and multimodal features during decision-making. This adaptive mechanism enables the model to distinguish the relative importance of different modal information, optimizing the decision-making process of the multimodal neural network and thereby improving detection accuracy. Experiments conducted on the public Chinese dataset Weibo and English dataset Twitter demonstrate that the proposed optimized network architecture outperforms other multimodal methods by 3% to 8% in terms of detection accuracy, validating the superiority of this neural network optimization-based approach for multimodal fake news detection tasks.