Multi-view Fake News Detection Model Based on Dynamic Hypergraph

• URL: http://arxiv.org/abs/2412.19227v1
• Date: Thu, 26 Dec 2024 14:05:51 GMT
• Title: Multi-view Fake News Detection Model Based on Dynamic Hypergraph
• Authors: Rongping Ye, Xiaobing Pei,
• Abstract summary: We propose a novel dynamic hypergraph-based multi-view fake news detection model (DHy-MFND) We employ hypergraph structures to model complex high-order relationships among multiple news pieces. We also introduce contrastive learning to capture authenticity-relevant embeddings across different views.
• Score: 1.1970409518725493
• License:
• 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.

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