  An important aspect of preventing fake news dissemination is to proactively
detect the likelihood of its spreading. Research in the domain of fake news
spreader detection has not been explored much from a network analysis
perspective. In this paper, we propose a graph neural network based approach to
identify nodes that are likely to become spreaders of false information. Using
the community health assessment model and interpersonal trust we propose an
inductive representation learning framework to predict nodes of
densely-connected community structures that are most likely to spread fake
news, thus making the entire community vulnerable to the infection. Using
topology and interaction based trust properties of nodes in real-world Twitter
networks, we are able to predict false information spreaders with an accuracy
of over 90%.
