Early detection of rumors based on source tweet-word graph attention networks.

ABSTRACT: The massively and rapidly spreading disinformation on social network platforms poses a serious threat to public safety and social governance. Therefore, early and accurate detection of rumors in social networks is of vital importance before they spread on a large scale. Considering the small-world property of social networks, the source tweet-word graph is decomposed from the global graph of rumors, and a rumor detection method based on graph attention network of source tweet-word graph is proposed to fully learn the structure of rumor propagation and the deep representation of text contents. Specifically, the proposed model can adequately capture the contextual semantic association representation of source tweets during the propagation and extract semantic features. For the data sparsenes

SUBMITTER: Jia H 

PROVIDER: S-EPMC9273096 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature