Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

TL;DR

This paper investigates the hierarchical propagation networks of news on social media to improve fake news detection by analyzing linguistic, structural, and temporal features, demonstrating their effectiveness.

Contribution

It introduces a hierarchical propagation network model for fake news detection and provides a comparative analysis of propagation features between fake and real news.

Findings

Propagation network features differ significantly between fake and real news.

Hierarchical propagation features improve fake news detection accuracy.

Feature importance analysis confirms the relevance of propagation features.

Abstract

Consuming news from social media is becoming increasingly popular. However, social media also enables the widespread of fake news. Because of its detrimental effects brought by social media, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection. In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature important analysis. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.