How much is too much? The difference between research influence and self-citation excess

Martin Szomszor, David A. Pendlebury and Jonathan Adams ()
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Martin Szomszor: Clarivate Analytics
David A. Pendlebury: Clarivate Analytics
Jonathan Adams: Clarivate Analytics

Scientometrics, 2020, vol. 123, issue 2, No 25, 1119-1147

Abstract: Abstract Citations can be an indicator of publication significance, utility, attention, visibility or short-term impact but analysts need to confirm whether a high citation count for an individual is a genuine reflection of influence or a consequence of extraordinary, even excessive, self-citation. It has recently been suggested there may be increasing misrepresentation of research performance by individuals who self-cite inordinately to achieve scores and win rewards. In this paper we consider self-referencing and self-citing, describe the typical shape of self-citation patterns for carefully curated publication sets authored by 3517 Highly Cited Researchers and quantify the variance in the distribution of self-citation rates within and between all 21 Essential Science Indicators’ fields. We describe both a generic level of median self-referencing rates, common to most fields, and a graphical, distribution-driven assessment of excessive self-citation that demarcates a threshold not dependent on statistical tests or percentiles (since for some fields all values are within a central ‘normal’ range). We describe this graphical procedure for identifying exceptional self-citation rates but emphasize the necessity for expert interpretation of the citation profiles of specific individuals, particularly in fields with atypical self-citation patterns.

Keywords: Self-citation; Citation analysis; Citation distribution; Outliers; Research evaluation; Self-references (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (14)

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DOI: 10.1007/s11192-020-03417-5

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