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    Volume 2, Issue 1, IECE Transactions on Intelligent Systematics
    Volume 2, Issue 1, 2025
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    IECE Transactions on Intelligent Systematics, 2025, Volume 2, Issue 1: 38-48

    Free Access | Review Article | 04 January 2025
    A Machine Learning-Based Scientometric Evaluation for Fake News Detection
    by
    IECE Author Hafiz Muhammad Zeeshan 1,2
    IECE Author Inam Ullah 3 *
    IECE Author Furkan Yousaf 4
    IECE Author Amin Sharafian 5
    IECE Author Md Belal Bin Heyat 6
    IECE Author Shazia Saqib 7
    IECE Author Atta Ur Rahman 8
    1 Department of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan
    2 Department of Computer Science, Superior University, Lahore, Pakistan
    3 Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
    4 School of Information Management, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Malaysia
    5 College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
    6 Shenzhen University, Shenzhen 518060, China
    7 Faculty of Information Technology, University of Central Punjab, Pakistan
    8 Interdisciplinary Research Centers for Finance and Digital Economy, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia
    * Corresponding Author: Inam Ullah, [email protected]
    DOI: 10.62762/TIS.2024.564569
    Received: 08 October 2024, Accepted: 11 December 2024, Published: 04 January 2025  
    Abstract
    In the modern world, disseminating false information is a problem that must be addressed, and algorithms based on machine learning are used to spot and stop the spread of incorrect information. Due to the current unregulated development of false news fabrication and dissemination, democracy is continuously under threat. Fake news may mislead individuals while influencing them because of its persuasiveness and life sciences. Using data from the Web of Science, this study undertakes a bibliometric analysis of research on the application of machine learning for fake news identification. The research underscores the need for a streamlined approach to analyze data exclusively from the Web of Science database. The suggested technique performs a thorough bibliometric study using Excel and the R tool Bibliometric. The study looks at variables in the field of machine learning for fake news detection, including publication volume, citations, collaborative research, and major research fields. According to the survey, Ashraf I is the most productive author in this discipline, which also names King Saud University as the most productive institution. IEEE Access is the most significant source of academic contributions.
    Graphical Abstract
    A Machine Learning-Based Scientometric Evaluation for Fake News Detection
    Keywords
    machine learning
    fake news detection
    bibliometric analysis
    information ecosystem
    Funding
    This work was supported without any funding.
    Cite This Article
    APA Style
    Zeeshan, H. M., Ullah, I., Yousaf, F., Sharafian, A., Heyat, M. B. B., Saqib, S., & Rahman, A. U. (2025). A Machine Learning-Based Scientometric Evaluation for Fake News Detection. IECE Transactions on Intelligent Systematics, 2(1), 38–48. https://doi.org/10.62762/TIS.2024.564569
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