Fake news spreader detection using trust-based strategies in social networks with bot filtration.

ABSTRACT: An important aspect of preventing fake news spreading in social networks is to proactively detect the users that are likely going to spread such news. Research in the domain of spreader detection is at a nascent stage compared to fake news detection. In this paper, we propose a graph neural network-based framework to identify nodes that are likely to become spreaders of false information. Using the community health assessment model and interpersonal trust (quantified using network topology and historical behavioral data), 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. We also analyze the performance of our model in the

SUBMITTER: Rath B 

PROVIDER: S-EPMC9244065 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature