MFAN: Multi-modal Feature-enhanced Attention Networks for Rumor Detection
Rumor spreaders are increasingly taking advantage of multimedia content to attract and mislead news consumers on social media. Although recent multimedia rumor detection models have exploited both textual and visual features for classifcation, they do not integrate the social structure features simultaneously, which have shown promising performance for rumor identifcation. It is challenging to combine the heterogeneous multi-modal data in consideration of their complex relationships. In this work, we propose a novel Multi-modal Featureenhanced Attention Networks (MFAN) for rumor detection, which makes the frst attempt to integrate textual, visual, and social graph features in one unifed framework. Specifcally, it considers both the complement and alignment relationships between different modalities to achieve better fusion. Moreover, it takes into account the incomplete links in the social network data due to data collection constraints and proposes to infer hidden links to learn better social graph features. The experimental results show that MFAN can detect rumors effectively and outperform state-of-the-art methods.
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