Detecting Fake News Spreaders in Social Networks using Inductive Representation Learning

TL;DR

This paper introduces a graph neural network approach leveraging community health and trust metrics to proactively identify social network nodes likely to spread fake news, achieving over 90% accuracy.

Contribution

It presents a novel inductive representation learning framework combining community health and trust metrics for fake news spreader detection in social networks.

Findings

Achieved over 90% accuracy in predicting fake news spreaders.

Utilized topology and trust properties of Twitter networks.

Proposed a new approach for early detection of false information spreaders.

Abstract

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%.