Detecting Fake News Spreaders in Social Networks using Inductive Representation Learning

Understand

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.