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Classification method for detecting coercive self-citation in journals

Author

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  • Yu, Tian
  • Yu, Guang
  • Wang, Ming-Yang

Abstract

Journal self-citations strongly affect journal evaluation indicators (such as impact factors) at the meso- and micro-levels, and therefore they are often increased artificially to inflate the evaluation indicators in journal evaluation systems. This coercive self-citation is a form of scientific misconduct that severely undermines the objective authenticity of these indicators. In this study, we developed the feature space for describing journal citation behavior and conducted feature selection by combining GA-Wrapper with RelifF. We also constructed a journal classification model using the logistic regression method to identify normal and abnormal journals. We evaluated the performance of the classification model using journals in three subject areas (BIOLOGY, MATHEMATICS and CHEMISTRY, APPLIED) during 2002–2011 as the test samples and good results were achieved in our experiments. Thus, we developed an effective method for the accurate identification of coercive self-citations.

Suggested Citation

  • Yu, Tian & Yu, Guang & Wang, Ming-Yang, 2014. "Classification method for detecting coercive self-citation in journals," Journal of Informetrics, Elsevier, vol. 8(1), pages 123-135.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:1:p:123-135
    DOI: 10.1016/j.joi.2013.11.001
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    References listed on IDEAS

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    9. Juan Miguel Campanario, 2011. "Large increases and decreases in journal impact factors in only one year: The effect of journal self‐citations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 230-235, February.
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    7. Taşkın, Zehra & Doğan, Güleda & Kulczycki, Emanuel & Zuccala, Alesia Ann, 2021. "Self-Citation Patterns of Journals Indexed in the Journal Citation Reports," Journal of Informetrics, Elsevier, vol. 15(4).
    8. Martin Szomszor & David A. Pendlebury & Jonathan Adams, 2020. "How much is too much? The difference between research influence and self-citation excess," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 1119-1147, May.
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    10. Thelwall, Mike & Wilson, Paul, 2014. "Regression for citation data: An evaluation of different methods," Journal of Informetrics, Elsevier, vol. 8(4), pages 963-971.

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