Rumor Detection Based on Cross-modal Information-enhanced Fusion Network

The rapid spread of information through online platforms and social media networks has led to an increase in the propagation of rumors, which can have detrimental effects. Researchers have proposed various deep learning models for multimodal rumor detection. However, these models often handle each modality individually, limiting the abilities of information complementation and modal enhancement. To address this challenge, we propose a Cross-modal Information-enhanced Fusion Network (CIFN) for rumor detection on social media platforms. CIFN enhances the representation of different modalities in a unified framework before effectively combining textual and visual information to accomplish the rumor detection task. Specifically, CIFN introduces the Feature Information Enhancement (FIE) module, which enhances different modal information by selectively focusing on relevant features and capturing interdependencies between modalities. Additionally, CIFN introduces a Review-based Fusion Mechanism (RFM) to integrate textual and visual features, considering the weight allocation of different modalities at the feature level. Extensive experiments conducted on two public datasets show that the proposed CIFN outperforms existing methods in rumor detection.