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An important aspect of preventing fake news dissemination is to proactivelydetect the likelihood of its spreading. Research in the domain of fake newsspreader detection has not been explored much from a network analysisperspective. In this paper, we propose a graph neural network based approach toidentify nodes that are likely to become spreaders of false information. Usingthe community health assessment model and interpersonal trust we propose aninductive representation learning framework to predict nodes ofdensely-connected community structures that are most likely to spread fakenews, thus making the entire community vulnerable to the infection. Usingtopology and interaction based trust properties of nodes in real-world Twitternetworks, we are able to predict false information spreaders with an accuracyof over 90%.

Detecting Fake News Spreaders in Social Networks using Inductive Representation Learning