  With the rapid development of mobile Internet technology and the widespread
use of mobile devices, it becomes much easier for people to express their
opinions on social media. The openness and convenience of social media
platforms provide a free expression for people but also cause new social
problems. The widespread of false rumors on social media can bring about the
panic of the public and damage personal reputation, which makes rumor automatic
detection technology become particularly necessary. The majority of existing
methods for rumor detection focus on mining effective features from text
contents, user profiles, and patterns of propagation. Nevertheless, these
methods do not take full advantage of global semantic relations of the text
contents, which characterize the semantic commonality of rumors as a key factor
for detecting rumors. In this paper, we construct a tweet-word-user
heterogeneous graph based on the text contents and the source tweet
propagations of rumors. A meta-path based heterogeneous graph attention network
framework is proposed to capture the global semantic relations of text
contents, together with the global structure information of source tweet
propagations for rumor detection. Experiments on real-world Twitter data
demonstrate the superiority of the proposed approach, which also has a
comparable ability to detect rumors at a very early stage.
