Exploring Fake News Detection with Heterogeneous Social Media Context Graphs

TL;DR

This paper introduces a novel approach to fake news detection by constructing heterogeneous social context graphs and applying graph neural networks, demonstrating superior results over traditional text-based methods.

Contribution

It proposes a new graph-based framework incorporating social context for fake news detection, exploring various information types and neural architectures.

Findings

Graph-based approach outperforms text-only methods

Different social context information levels impact detection accuracy

Robust results achieved on benchmark datasets

Abstract

Fake news detection has become a research area that goes way beyond a purely academic interest as it has direct implications on our society as a whole. Recent advances have primarily focused on textbased approaches. However, it has become clear that to be effective one needs to incorporate additional, contextual information such as spreading behaviour of news articles and user interaction patterns on social media. We propose to construct heterogeneous social context graphs around news articles and reformulate the problem as a graph classification task. Exploring the incorporation of different types of information (to get an idea as to what level of social context is most effective) and using different graph neural network architectures indicates that this approach is highly effective with robust results on a common benchmark dataset.