Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information

TL;DR

This paper introduces Hetero-SCAN, a novel framework that leverages meta-paths to effectively incorporate multi-level social context and temporal information for improved fake news detection.

Contribution

It proposes a meta-path-based approach to utilize multi-level social context and temporal data without information loss, enabling end-to-end news representation learning.

Findings

Hetero-SCAN outperforms existing fake news detection methods.

Meta-Path effectively captures semantics in heterogeneous social graphs.

End-to-end learning improves detection accuracy.

Abstract

Fake news, false or misleading information presented as news, has a significant impact on many aspects of society, such as in politics or healthcare domains. Due to the deceiving nature of fake news, applying Natural Language Processing (NLP) techniques to the news content alone is insufficient. The multi-level social context information (news publishers and engaged users in social media) and temporal information of user engagement are important information in fake news detection. The proper usage of this information, however, introduces three chronic difficulties: 1) multi-level social context information is hard to be used without information loss, 2) temporal information is hard to be used along with multi-level social context information, 3) news representation with multi-level social context and temporal information is hard to be learned in an end-to-end manner. To overcome all three difficulties, we propose a novel fake news detection framework, Hetero-SCAN. We use Meta-Path to extract meaningful multi-level social context information without loss. Meta-Path, a composite relation connecting two node types, is proposed to capture the semantics in the heterogeneous graph. We then propose Meta-Path instance encoding and aggregation methods to capture the temporal information of user engagement and produce news representation end-to-end. According to our experiment, Hetero-SCAN yields significant performance improvement over state-of-the-art fake news detection methods.