{"title":"可持续信号:用于假新闻检测的异构图神经框架","authors":"Adil Mudasir Malla, Asif Ali Banka","doi":"10.1007/s13198-024-02415-7","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sustainable signals: a heterogeneous graph neural framework for fake news detection\",\"authors\":\"Adil Mudasir Malla, Asif Ali Banka\",\"doi\":\"10.1007/s13198-024-02415-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":14463,\"journal\":{\"name\":\"International Journal of System Assurance Engineering and Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of System Assurance Engineering and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s13198-024-02415-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02415-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.