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 to identify nodes that are likely to become spreaders of false information . Using topology and interaction based trust properties of nodes in real-world Twitternetworks, we are able to predict false information spreaders with an accuracy of over 90% . Using the 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 .

Author(s) : Bhavtosh Rath, Aadesh Salecha, Jaideep Srivastava

Links : PDF - Abstract

Code :

https://github.com/afansi/multiscalegnn


Coursera

Keywords : spreaders - nodes - propose - information - community -

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