Detecting rumors on social media has become particular important due to the rapid dissemination and adverse impacts on our lives. Though a set of rumor detection models have exploited the message propagation structural or temporal information, they seldom model them altogether to enjoy the best of both worlds. Moreover, the dynamics of knowledge information associated with the comments are not involved, either. To this end, we propose a novel Dual-Dynamic Graph Convolutional Networks, termed as DDGCN, which can model the dynamics of messages in propagation as well as the dynamics of the background knowledge from Knowledge graphs in one unified framework. Specifically, two Graph Convolutional Networks are adopted to capture the above two types of structure information at different time stages, which are then combined with a temporal fusing unit. This allows for learning the dynamic event representations in a more fine-grained manner, and incrementally aggregating them to capture the cascading effect for better rumor detection. Extensive experiments on two public real-world datasets demonstrate that our proposal yields significant improvements compared to strong baselines and can detect rumors at early stages.

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 classification, they do not integrate the social structure features simultaneously, which have shown promising performance for rumor identification. 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 Feature-enhanced Attention Networks (MFAN) for rumor detection, which makes the first attempt to integrate textual, visual, and social graph features in one unified framework. Specifically, 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.