MACCN: Multi-Modal Adaptive Co-Attention Fusion Contrastive Learning Networks for Fake News Detection

Abstract

With the rapid proliferation of social networks, individuals now have greater access to news with increased speed. Simultaneously, there has been a heightened emphasis on detecting and mitigating the dissemination of fake news. One notable limitation of existing fake news detection models is their inability to effectively integrate multi-modal features, as they typically only establish connections between unimodal features, neglecting the potential synergies and complementarity among different modes. To address this issue, we introduce the Multi-modal Adaptive Co-attention fusion Contrastive learning Network (MACCN) for enhancing the detection of fake news by improving the fusion of textual and visual features. Our approach commences by employing distinct encoders to construct a high-level feature space for each modality. Subsequently, the Adaptive Co-attention Fusion Network is employed to establish strong correlations between textual and visual features, leading to a comprehensive representation. Ultimately, the model's performance is further enhanced through the application of contrastive learning, resulting in a more precise detection of fake news. We conducted an extensive series of experiments on three diverse datasets, and the results conclusively demonstrate that MACCN adeptly captures the interplay between multi-modal features, surpassing the performance of state-of-the-art methods.