Multi-view Fake News Detection Model Based on Dynamic Hypergraph

TL;DR

This paper introduces a dynamic hypergraph-based multi-view model for fake news detection that captures complex relationships across text, propagation, and hypergraph structures, improving detection accuracy.

Contribution

The paper proposes a novel dynamic hypergraph approach with multi-view learning and contrastive learning for more effective fake news detection.

Findings

Outperforms existing methods on benchmark datasets

Effectively models high-order relationships among news pieces

Demonstrates robustness across different datasets

Abstract

With the rapid development of online social networks and the inadequacies in content moderation mechanisms, the detection of fake news has emerged as a pressing concern for the public. Various methods have been proposed for fake news detection, including text-based approaches as well as a series of graph-based approaches. However, the deceptive nature of fake news renders text-based approaches less effective. Propagation tree-based methods focus on the propagation process of individual news, capturing pairwise relationships but lacking the capability to capture high-order complex relationships. Large heterogeneous graph-based approaches necessitate the incorporation of substantial additional information beyond news text and user data, while hypergraph-based approaches rely on predefined hypergraph structures. To tackle these issues, we propose a novel dynamic hypergraph-based multi-view fake news detection model (DHy-MFND) that learns news embeddings across three distinct views: text-level, propagation tree-level, and hypergraph-level. By employing hypergraph structures to model complex high-order relationships among multiple news pieces and introducing dynamic hypergraph structure learning, we optimize predefined hypergraph structures while learning news embeddings. Additionally, we introduce contrastive learning to capture authenticity-relevant embeddings across different views. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our proposed DHy-MFND compared with a broad range of competing baselines.