可持续信号:用于假新闻检测的异构图神经框架

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal of System Assurance Engineering and Management Pub Date : 2024-07-05 DOI:10.1007/s13198-024-02415-7
Adil Mudasir Malla, Asif Ali Banka
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引用次数: 0

摘要

数字技术加剧了假新闻的传播,导致误解、误解和经济挑战。在人工智能进步的推动下,研究人员已经开发出利用各种数据特征识别虚假信息的自动化技术。大多数算法侧重于新闻本身及其上下文的信号,往往忽略了用户的偏好。根据确认偏差理论,个人更有可能传播与其信念一致的虚假信息。用户的历史和社交活动(如他们的发帖)有助于识别假新闻,并为他们的新闻选择提供参考。然而,将用户偏好纳入假新闻检测的研究还很有限。本研究引入了一个基于图神经网络(GNN)和自然语言模型的框架,从图和内容两个角度捕捉信号,同时考虑用户偏好。我们之所以选择图神经网络,是因为它能够对图结构数据中的复杂关系进行建模。具体来说,我们使用图注意力网络是因为它能够权衡不同节点的重要性,从而增强对相关信号的捕捉。该框架通过分析社交活动和新闻选择来整合用户偏好。在真实世界数据集上的实验结果表明,我们的模型达到了 98% 的准确率。我们的模型甚至超过了那些不考虑用户偏好的模型。这些发现凸显了利用用户偏好加强假新闻检测的潜力,为解决信息污染问题提供了一种更稳健的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Sustainable signals: a heterogeneous graph neural framework for fake news detection

Digital technology has increased the spread of fake news, leading to misconceptions, misunderstandings, and economic challenges. Researchers have developed automated techniques to identify false information using various data features, driven by advancements in AI. Most algorithms focus on signals from the news itself and its context, often ignoring user preferences. According to confirmation bias theory, individuals are more likely to spread false information that aligns with their beliefs. Users’ historical and social activities, such as their postings, can help identify fake news and inform their news choices. However, there is limited research on incorporating user preferences in fake news detection. This study introduces a framework based on Graph Neural Networks (GNNs) and natural language models to capture signals from both graph and content perspectives, considering user preferences. We chose GNNs for their ability to model complex relationships in graph-structured data. Specifically, we used the Graph Attention Network due to its ability to weigh the importance of different nodes, enhancing the capture of relevant signals. The framework integrates user preferences by analyzing social activities and news choices. Experimental results on a real-world dataset show our model achieves an accuracy of 98%. Outperforming models that do even consider user preferences. These findings highlight the potential of leveraging user preferences to enhance fake news detection, offering a more robust approach to tackling information pollution.

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来源期刊
CiteScore
4.30
自引率
10.00%
发文量
252
期刊介绍: This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems. Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
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