  The development of social media has revolutionized the way people
communicate, share information and make decisions, but it also provides an
ideal platform for publishing and spreading rumors. Existing rumor detection
methods focus on finding clues from text content, user profiles, and
propagation patterns. However, the local semantic relation and global
structural information in the message propagation graph have not been well
utilized by previous works.
  In this paper, we present a novel global-local attention network (GLAN) for
rumor detection, which jointly encodes the local semantic and global structural
information. We first generate a better integrated representation for each
source tweet by fusing the semantic information of related retweets with the
attention mechanism. Then, we model the global relationships among all source
tweets, retweets, and users as a heterogeneous graph to capture the rich
structural information for rumor detection. We conduct experiments on three
real-world datasets, and the results demonstrate that GLAN significantly
outperforms the state-of-the-art models in both rumor detection and early
detection scenarios.
