  Social media has been developing rapidly in public due to its nature of
spreading new information, which leads to rumors being circulated. Meanwhile,
detecting rumors from such massive information in social media is becoming an
arduous challenge. Therefore, some deep learning methods are applied to
discover rumors through the way they spread, such as Recursive Neural Network
(RvNN) and so on. However, these deep learning methods only take into account
the patterns of deep propagation but ignore the structures of wide dispersion
in rumor detection. Actually, propagation and dispersion are two crucial
characteristics of rumors. In this paper, we propose a novel bi-directional
graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to
explore both characteristics by operating on both top-down and bottom-up
propagation of rumors. It leverages a GCN with a top-down directed graph of
rumor spreading to learn the patterns of rumor propagation, and a GCN with an
opposite directed graph of rumor diffusion to capture the structures of rumor
dispersion. Moreover, the information from the source post is involved in each
layer of GCN to enhance the influences from the roots of rumors. Encouraging
empirical results on several benchmarks confirm the superiority of the proposed
method over the state-of-the-art approaches.
