The rapid development of online social networks significantly facilitates the interaction of people and dramatically expands the diffusion sphere of information. Rumours, however, are not excluded from the list of beneficiaries. The widespread of rumours has lengthened the psychological distance and caused tremendous economic losses, and rumour detection has become an inescapable and challenging task of great practical importance. In this work, we propose a novel neural network architecture for rumour classification and early rumour detection of fine-grained categories. Unlike using tree-like modules for structural feature extraction, we build an information stream network and employ graph convolutional networks to explore the relations among the hierarchical nodes in the network. To enhance the sequential representation learning, the module of deep bilateral gated recurrent units is further incorporated to reveal the crucial features hiding behind the information flow. Moreover, to organically fuse the learned high-dimensionally structural and sequential features, attention mechanism is applied to automatically adjust the trainable weights. Comparative experiments on two real-world datasets demonstrate that our proposed model outperforms the state-of-the-art methods in the task of fine-grained rumour classification and is capable of identifying rumours at an early stage.
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