A Multimodal Fake News Detection Model Based on Bidirectional Semantic Enhancement and Adversarial Network Under Web3.0

Abstract

Web3.0 aims to foster a trustworthy environment enabling user trust and content verifiability. However, the proliferation of fake news undermines this trust and disrupts social ecosystems, making the effective alignment of visual-textual semantics and accurate content verification a pivotal challenge. Existing methods still struggle with deep cross-modal interaction and the adaptive calibration of discrepancies. To address this, we introduce the Bidirectional Semantic Enhancement and Adversarial Network (BSEAN). BSEAN first extracts features using large pre-trained models: a hybrid encoder for text and the Swin Transformer for images. It then employs a Bidirectional Modality Mapping Network, governed by cycle consistency, to achieve preliminary semantic alignment. Building on this, a Semantic Enhancement and Calibration Network explores inter-modal dependencies and quantifies semantic deviations to enhance discriminative capability. Finally, a Dual Adversarial Learning framework bolsters event generalization and representation consistency through adversarial training with event and modality discriminators. Experiments on public Weibo and Twitter datasets validate BSEAN’s superior performance across all metrics, demonstrating its efficacy in tackling the complex challenges of deep cross-modal interaction and dynamic modality calibration within Web3.0 social networks.