A Graph Convolutional Encoder and Decoder Model for Rumor Detection

With the development of technology and the expansion of social media, rumors spread widely and the rumor detection has gradually caused widespread concern. The early method of using handcrafted features has been eliminated due to inefficiency, and deep learning methods have been gradually adopted in recent years. However, most of the methods only consider content information such as text, which is often not enough for the specific field, rumor detection. Some studies take propagation rule into consideration, such as Kernel-based, RvNN. In addition, the structure formed via propagation of rumors and non-rumors have different properties. Compared with dynamic propagation, structure here is the final result of propagation and it's static and global. In order to enhance the structure information, we proposes a model that obtains textual, propagation and structure information. The model contains three components: Encoder, Decoder, and Detector. The encoder uses the efficient Graph Convolutional Network to regard the initial text as input and update the representation through propagation to learn text and propagation information. Then the encoded representation would be used for subsequent decoder which uses AutoEncoder to learn the overall structure information. Simultaneously, the detector utilizes the output of encoder to classify events as fake or not. These three modules are jointly trained to improve the model effect. We verified our method on three real-world datasets, and the results show that our method outperforms other state-of-the-art methods.