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

Martin Szomszor,Jonathan Adams,David A Pendlebury

doi:10.1007/s11192-020-03417-5

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.

Highlights

In this paper we report our investigations as to whether there is a typical or ‘normal range’ of self-citation for each of 21 discipline-based fields employed in Essential Science Indicators (ESI: these are listed in the “Appendix” Table 1) and we describe a graphical test for significant outliers
It is evident that the modal self-reference share is around 5–7% and the modal self-citation count is less than 5%, but this will be shown to vary by field
The work we report here has focussed on a particular tranche of researchers: individuals with a significant portfolio of papers that all lie within the 1% most highly cited for their field

Summary

Introduction

This paper introduces a graphical method for testing for indicative excessive author selfcitation and distinguishing this, via informed data review, from the true performance of the most influential researchers. The question is pertinent because self-citation has featured in recent publications that address the possible misrepresentation of research performance by individuals and, attempts to game citation scores (Baccini et al 2019; D’Antuono and Ciavarella 2019; Ioannidis et al 2019; Kacem et al 2019; Peroni et al 2019; Seeber et al 2019). In this paper, that we refer to self-references and to self-cites and that they are not the same thing. The rate at which authors self-reference can be a guide to cultural norms (and to outliers) as much as is the frequency of self-citing

Methods

Results

Conclusion