Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

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…

• T
  Tencent
• NN
  National Natural Science Foundation of China

  Awards: 61972219, 61773229
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2018A030313422
• TU
  Tsinghua University

  Award: HW2018002