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RTrust: toward robust trust evaluation framework for fake news detection in online social networks

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Abstract

The Web 3.0 era is reshaping the way we share data, and online social networks play an essential role in this process. In online social networks (OSN), the interaction between users is closely related to data security. With the widespread use of OSN, the harm caused by fake news has penetrated many corners of society. Most existing graph machine learning works for fake news detection focus only on the news propagation path or news content itself; they ignore the trust relationships between users and the ease of attack of the graph formed by the propagation path. The underlying trust factors among users can be revealed by their endogenous preferences that help indicate the extent to which others are expected to perform particular actions. Moreover, joint trainable news propagation paths and social trust can improve the robustness of graph network models and slow the accumulation of errors caused by fraudulent messaging. However, these works are somewhat limited in fake news detection. This paper proposes novel robust trust evaluation architecture for fake news detection, RTrust, which improves the performance of fake news detection by incorporating trust propagation and robustness. Comparative results from the latest baseline on two real-world datasets demonstrate the advantages of RTrust in detecting fake news and its robustness.

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Data Availability

No datasets were generated or analysed during the current study.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62172160 and Grant 62062034, in part by the Jiangxi Provincial Natural Science Foundation under Grant 20212ACB212002, and in part by the Excellent Scientific and Technological Innovation Teams of Jiangxi Province under Grant 20181BCB24009.

Author information

Authors and Affiliations

  1. College of Information Engineering, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

    Nan Jiang, Ziang Tu, Hualin Zhan, Jiahui Zhao & Weihao Gu

  2. School of Mathematics and Statistics, the University of Sydney, Camperdown, 2006, NSW, Australia

    Kanglu Pei

  3. College of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

    Jie Wen

  4. College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, Fujian, China

    Ximeng Liu

  5. School of Control Science and Engineering, Dalian University of Technology, Street, Dalian, 116024, Liaoning, China

    Sen Qiu

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  1. Nan Jiang

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Contributions

N.J. : Model and algorithm design, implementation, evaluation and analysis of results. Z.T. : writes the paper draft and final version. K.P. : data curation. J.W. : results analysis and discussions. H.Z. : conductes the ablation study. J.Z. : Conceptualisation. W.G.: experimental discussions. X.L.: provides valuable suggestions to improve the algorithms. S.Q. : proofreads the manuscript.

Corresponding author

Correspondence toNan Jiang.

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Not applicable. We declare that this paper does not involve any human or animal studies, so no ethical issues are involved.

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The authors declare no competing interests.

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Jiang, N., Tu, Z., Pei, K.et al. RTrust: toward robust trust evaluation framework for fake news detection in online social networks.World Wide Web27, 76 (2024). https://doi.org/10.1007/s11280-024-01317-9

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