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A Large-Scale Analysis of Impact Factor Biased Journal Self-Citations

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  • Caspar Chorus
  • Ludo Waltman

Abstract

Based on three decades of citation data from across scientific fields of science, we study trends in impact factor biased self-citations of scholarly journals, using a purpose-built and easy to use citation based measure. Our measure is given by the ratio between i) the relative share of journal self-citations to papers published in the last two years, and ii) the relative share of journal self-citations to papers published in preceding years. A ratio higher than one suggests that a journal’s impact factor is disproportionally affected (inflated) by self-citations. Using recently reported survey data, we show that there is a relation between high values of our proposed measure and coercive journal self-citation malpractices. We use our measure to perform a large-scale analysis of impact factor biased journal self-citations. Our main empirical result is, that the share of journals for which our measure has a (very) high value has remained stable between the 1980s and the early 2000s, but has since risen strongly in all fields of science. This time span corresponds well with the growing obsession with the impact factor as a journal evaluation measure over the last decade. Taken together, this suggests a trend of increasingly pervasive journal self-citation malpractices, with all due unwanted consequences such as inflated perceived importance of journals and biased journal rankings.

Suggested Citation

  • Caspar Chorus & Ludo Waltman, 2016. "A Large-Scale Analysis of Impact Factor Biased Journal Self-Citations," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-11, August.
    • Handle: RePEc:plo:pone00:0161021
    DOI: 10.1371/journal.pone.0161021
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    References listed on IDEAS

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    1. Lidia González & Juan Miguel Campanario, 2007. "Structure of the impact factor of journals included in the Social Sciences Citation Index: Citations from documents labeled “editorial material”," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(2), pages 252-262, January.
    2. 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.
    3. Richard Van Noorden, 2013. "Brazilian citation scheme outed," Nature, Nature, vol. 500(7464), pages 510-511, August.
    4. Juan Miguel Campanario & Lidia González, 2006. "Journal self-citations that contribute to the impact factor: Documents labeled “editorial material” in journals covered by the Science Citation Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(2), pages 365-386, November.
    5. Petr Heneberg, 2016. "From Excessive Journal Self-Cites to Citation Stacking: Analysis of Journal Self-Citation Kinetics in Search for Journals, Which Boost Their Scientometric Indicators," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-20, April.
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