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Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph

Author

Listed:
  • Renata Avros

    (Software Engineering Department, Braude College of Engineering, Karmiel 21982, Israel)

  • Saar Keshet

    (Software Engineering Department, Braude College of Engineering, Karmiel 21982, Israel)

  • Dvora Toledano Kitai

    (Software Engineering Department, Braude College of Engineering, Karmiel 21982, Israel)

  • Evgeny Vexler

    (Software Engineering Department, Braude College of Engineering, Karmiel 21982, Israel)

  • Zeev Volkovich

    (Software Engineering Department, Braude College of Engineering, Karmiel 21982, Israel)

Abstract

Ensuring the integrity of scientific literature is essential for advancing knowledge and research. However, the credibility and trustworthiness of scholarly publications are compromised by manipulated citations. Traditional methods, such as manual inspection and basic statistical analyses, have limitations in detecting intricate patterns and subtle manipulations of citations. In recent years, network-based approaches have emerged as promising techniques for identifying and understanding citation manipulation. This study introduces a novel method to identify potential citation manipulation in academic papers using perturbations of a deep embedding model. The key idea is to reconstruct meaningful connections represented by citations within a network by exploring, to some extent, longer alternative paths. These indirect pathways enable the recovery of reliable citations while estimating their trustworthiness. The investigation takes a comprehensive approach to link prediction, leveraging the consistent behavior of prominent connections when exposed to network perturbations. Through numerical experiments, the method demonstrates a high capability to identify reliable citations as the core of the analyzed data and to raise suspicions about unreliable references that may have been manipulated. This research presents a refined method for tackling the urgent problem of citation manipulation in academic papers. It harnesses statistical sampling and graph-embedding techniques to evaluate the credibility of scholarly publications with a substantial assessment of the whole citation graph.

Suggested Citation

  • Renata Avros & Saar Keshet & Dvora Toledano Kitai & Evgeny Vexler & Zeev Volkovich, 2023. "Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3820-:d:1233938
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    References listed on IDEAS

    as
    1. Jonathan D. Wren & Constantin Georgescu, 2022. "Detecting anomalous referencing patterns in PubMed papers suggestive of author-centric reference list manipulation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5753-5771, October.
    2. Chandra G. Prabha, 1983. "Some aspects of citation behavior: A pilot study in business administration," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 34(3), pages 202-206, May.
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