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. 2018 Sep 26;13(9):e0195773.
doi: 10.1371/journal.pone.0195773. eCollection 2018.

Self-citation is the hallmark of productive authors, of any gender

Shubhanshu Mishra  1 , Brent D Fegley  1   2 , Jana Diesner  1 , Vetle I Torvik  1
Affiliations

Self-citation is the hallmark of productive authors, of any gender

Shubhanshu Mishra et al. PLoS One. .

Abstract

It was recently reported that men self-cite >50% more often than women across a wide variety of disciplines in the bibliographic database JSTOR. Here, we replicate this finding in a sample of 1.6 million papers from Author-ity, a version of PubMed with computationally disambiguated author names. More importantly, we show that the gender effect largely disappears when accounting for prior publication count in a multidimensional statistical model. Gender has the weakest effect on the probability of self-citation among an extensive set of features tested, including byline position, affiliation, ethnicity, collaboration size, time lag, subject-matter novelty, reference/citation counts, publication type, language, and venue. We find that self-citation is the hallmark of productive authors, of any gender, who cite their novel journal publications early and in similar venues, and more often cross citation-barriers such as language and indexing. As a result, papers by authors with short, disrupted, or diverse careers miss out on the initial boost in visibility gained from self-citations. Our data further suggest that this disproportionately affects women because of attrition and not because of disciplinary under-specialization.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: Vetle Torvik is a co-inventor of the Author-ity dataset, which is subject to licensing to third-parties (for non-academic or for-profit use) by the University of Illinois, which owns this intellectual property. However, the Author-ity dataset is provided free-of-charge for non-profit academic research. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Self-citation rates as functions of author age as measured by prior publication count (top panels).
The horizontal lines show the overall self-citation rates. The bottom panels show the cumulate distributions of author age.
Fig 2
Fig 2. Change in effect of gender at each model-fitting step.
The sub-figures show the contribution of gender at each step in the iterative process of fitting and evaluating combinations of factors; only the model at the final step is the best-fitting among them. In both models, confounding factors ultimately minimize the effect of gender in self-citation; the most influential of them is author’s publication count (note Table 6). Y-axis is on log scale.
Fig 3
Fig 3. Change in odds with respect to mentioned values (in parentheses) of self-citation for select predictors of models of first and last authors.
Shaded regions indicate 95% confidence intervals. Y-axis is on log scale.
Fig 4
Fig 4. Change in odds with respect to mentioned values of self-citation for select predictors of models of first and last authors.
Error bars indicate 95% confidence intervals. Among other interesting points, note that the likelihood of self-citation is least for last authors with non-USA affiliation, implying that self-citing is customary among USA authors. X-axis is on log scale.
Fig 5
Fig 5. Author expertise as a function of prior publication count.
Expertise of an author on a given paper is measured by the proportion of subjects (MeSH; a paper typically has a dozen or so terms) on which the author has previously published. Expertise naturally grows with age but never reaches 100% because authors tend to publish on some topics that are new to them.
See this image and copyright information in PMC

References

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