基于动态超图的多视角假新闻检测模型
Multi-view Fake News Detection Model Based on Dynamic Hypergraph
AI总结：
本文提出动态超图多视角假新闻检测模型DHy-MFND，融合文本、传播树和超图三视角学习新闻嵌入，并通过动态超图结构学习与对比学习提升检测性能。
摘要：
随着在线社交网络的快速发展和内容审核机制的不足，假新闻检测已成为公众面临的紧迫问题。目前已提出多种假新闻检测方法，包括基于文本的方法以及一系列基于图的方法。然而，假新闻的欺骗性使得基于文本的方法效果较差。基于传播树的方法关注单条新闻的传播过程，能够捕捉成对关系，但缺乏捕捉高阶复杂关系的能力。大规模异构图方法需要引入大量超出新闻文本和用户数据的额外信息，而基于超图的方法则依赖预定义的超图结构。为解决这些问题，我们提出了一种新颖的基于动态超图的多视角假新闻检测模型（DHy-MFND），该模型在三个不同视角上学习新闻嵌入：文本级、传播树级和超图级。通过采用超图结构对多条新闻之间的复杂高阶关系进行建模，并引入动态超图结构学习，我们在学习新闻嵌入的同时优化预定义的超图结构。此外，我们引入对比学习来捕捉不同视角间与真实性相关的嵌入。在两个基准数据集上的大量实验表明，与广泛的竞争基线相比，我们提出的DHy-MFND具有显著的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
