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Is Self-Citation Biased? An Investigation via the Lens of Citation Polarity, Density, and Location

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Abstract

Traditional citation analysis methods have been criticized because their theoretical base of statistical counts does not reflect the motive or judgment of citing authors. In particular, self-citations may give undue credits to a cited article or mislead scientific development. This research aims to answer the question of whether self-citation is biased by probing into the motives and context of citations. It takes an integrated and fine-grained view of self-citations by examining them via multiple lenses — polarity, density, and location of citations. In addition, it explores potential moderating effects of citation level and associations among location contexts of citations to the same references for the first time. We analyzed academic publications across different topics and disciplines using both qualitative and quantitative methods. The results provide evidence that self-citations are free of bias in terms of citation density and polarity uncertainty, but they can be biased with respect to positivity and negativity of citations. Furthermore, this study reveals impacts of self-citing behavior on some citation patterns involving citation density, location concentration, and associations. The examination of self-citing behavior from those new perspectives shed new lights on the nature and function of self-citing behavior.

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Acknowledgements

The authors would like to thank the researchers and students who provided assistance with encoding the content and context of paper citations. This research is supported in part by the National Science Foundation (SES-152768, CNS-1704800). Any opinions, findings or recommendations expressed here are those of the authors and are not necessarily those of the sponsors of this research.

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Authors and Affiliations

  1. Department of Business Information Systems and Operations Management, The University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA

    Lina Zhou & Dongsong Zhang

  2. Department of Technology Innovation Management, Northcentral University, 2488 Historic Decatur Rd., Suite 100, San Diego, CA, 92106, USA

    Uchechukwuka Amadi

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  1. Lina Zhou
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  2. Uchechukwuka Amadi
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  3. Dongsong Zhang
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Correspondence to Lina Zhou.

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Zhou, L., Amadi, U. & Zhang, D. Is Self-Citation Biased? An Investigation via the Lens of Citation Polarity, Density, and Location. Inf Syst Front 22, 77–90 (2020). https://doi.org/10.1007/s10796-018-9889-9

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