Beyond News Contents: The Role of Social Context for Fake News Detection

TL;DR

This paper introduces TriFN, a novel framework that leverages social context, including publisher and user relationships, to improve fake news detection beyond content analysis alone.

Contribution

The paper presents a new tri-relationship embedding framework that models publisher-news and user-news relations to enhance fake news detection accuracy.

Findings

TriFN significantly outperforms baseline methods on real-world datasets.

Social context modeling improves fake news detection accuracy.

Publisher and user relationships are effective auxiliary signals.

Abstract

Social media is becoming popular for news consumption due to its fast dissemination, easy access, and low cost. However, it also enables the wide propagation of fake news, i.e., news with intentionally false information. Detecting fake news is an important task, which not only ensures users to receive authentic information but also help maintain a trustworthy news ecosystem. The majority of existing detection algorithms focus on finding clues from news contents, which are generally not effective because fake news is often intentionally written to mislead users by mimicking true news. Therefore, we need to explore auxiliary information to improve detection. The social context during news dissemination process on social media forms the inherent tri-relationship, the relationship among publishers, news pieces, and users, which has potential to improve fake news detection. For example, partisan-biased publishers are more likely to publish fake news, and low-credible users are more likely to share fake news. In this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world datasets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection.