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Analysing Author Self-citations in Computer Science Publications

  • Tobias Milz
  • Christin Seifert
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 903)

Abstract

In scientific papers, citations refer to relevant previous work in order to underline the current line of argumentation, compare to other work and/or avoid repetition in writing. Self-citations, e.g. authors citing own previous work might have the same motivation but have also gained negative attention w.r.t. unjustified improvement of scientific performance indicators. Previous studies on self-citations do not provide a detailed analysis in the domain of computer science. In this work, we analyse the prevalence of self-citations in the DBLP, a digital library for computer science. We find, that approx. 10% of all citations are self-citations, while the rates vary with year after publication and the position of the author in the list as well as with the gender of the lead author. Further, we find that C-ranked venues have the highest incoming self-citation rate, while the outgoing rate is stable across all ranks.

Keywords

Citations Self-citations Analysis DBLP CORE 

Notes

Acknowledgment

We would like to thank Moritz Grünbauer for his preliminary analysis and help with constructing the queries for the graph database.

References

  1. 1.
    Aksnes, D.W.: A macro study of self-citation. Scientometrics 56(2), 235–246 (2003)CrossRefGoogle Scholar
  2. 2.
    Alonso, S., Cabrerizo, F., Herrera-Viedma, E., Herrera, F.: h-index: a review focused in its variants, computation and standardization for different scientific fields. J. Inf. 3(4), 273–289 (2009)Google Scholar
  3. 3.
    Bartneck, C., Kokkelmans, S.: Detecting h-index manipulation through self-citation analysis. Scientometrics 87(1), 85–98 (2010)CrossRefGoogle Scholar
  4. 4.
    Costas, R., van Leeuwen, T.N., Bordons, M.: Self-citations at the meso and individual levels: effects of different calculation methods. Scientometrics 82, 517–537 (2010)CrossRefGoogle Scholar
  5. 5.
    Ferrara, E., Romero, A.E.: Scientific impact evaluation and the effect of self-citations: Mitigating the bias by discounting the h-index. JASIST 64(11), 2332–2339 (2013)CrossRefGoogle Scholar
  6. 6.
    Fowler, J.H., Aksnes, D.W.: Does self-citation pay? Scientometrics 72(3), 427–437 (2007)CrossRefGoogle Scholar
  7. 7.
    Gauch Jr., H.G.: Scientific Method in Practice. Cambridge University Press, Cambridge (2002).  https://doi.org/10.1017/CBO9780511815034CrossRefGoogle Scholar
  8. 8.
    Ghiasi, G., Larivère, V., Sugimoto, C.R.: Gender differences in synchronous and diachronous self-citations. In: Proceedings International Conference on Science and Technology Indicaors (2016)Google Scholar
  9. 9.
    Glänzel, W., Debackere, K., Thijs, B., Schubert, A.: A concise review on the role of author self-citations in information science, bibliometrics and science policy. Scientometrics 67, 263–277 (2006)CrossRefGoogle Scholar
  10. 10.
    Hemmat Esfe, M., Wongwises, S., Asadi, A., Karimipour, A., Akbari, M.: Mandatory and self-citation; types, reasons, their benefits and disadvantages. Sci. Eng. Ethics 21, 1581–1585 (2015)CrossRefGoogle Scholar
  11. 11.
    Ioannidis, J.P.: A generalized view of self-citation: direct, co-author, collaborative, and coercive induced self-citation. J. Psychosom. Res. 78, 7–11 (2015)CrossRefGoogle Scholar
  12. 12.
    King, M.M., Bergstrom, C.T., Correll, S.J., Jacquet, J., West, J.D.: Men set their own cites high: gender and self-citation across fields and over time. Socius 3 (2017)Google Scholar
  13. 13.
    Larivire, V., Ni, C., Gingras, Y., Cronin, B., Sugimoto, C.: Bibliometrics: global gender disparities in science. Nature 504, 211–213 (2013)CrossRefGoogle Scholar
  14. 14.
    Lawani, S.M.: On the heterogeneity and classification of author self-citations. J. Am. Soc. Inf. Sci. 33(5), 281–284 (1982)CrossRefGoogle Scholar
  15. 15.
    Leblond, M.: Author self-citations in the field of ecology. Scientometrics 91, 943–953 (2012)CrossRefGoogle Scholar
  16. 16.
    Lee, D., On, B.W., Kang, J., Park, S.: Effective and scalable solutions for mixed and split citation problems in digital libraries. In: Proceedings of the International Workshop on Information Quality in Information Systems, pp. 69–76. ACM, New York (2005)Google Scholar
  17. 17.
    Ley, M.: The DBLP computer science bibliography: evolution, research issues, perspectives. In: Laender, A.H.F., Oliveira, A.L. (eds.) SPIRE 2002. LNCS, vol. 2476, pp. 1–10. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45735-6_1CrossRefGoogle Scholar
  18. 18.
    Medoff, H.M.: The efficiency of self-citations in economics. Scientometrics 69, 69–84 (2006)CrossRefGoogle Scholar
  19. 19.
    Müller, M.C., Reitz, F., Roy, N.: Data sets for author name disambiguation: an empirical analysis and a new resource. Scientometrics (2017).  https://doi.org/10.1007/s11192-017-2363-5
  20. 20.
    Purvis, A.: The h index: playing the numbers game. Trends Ecol. Evol. 21(8), 422 (2006).  https://doi.org/10.1016/j.tree.2006.05.014CrossRefGoogle Scholar
  21. 21.
    Schubert, A., Glänzel, W., Thijs, B.: The weight of author self-citations. A fractional approach to self-citation counting. Scientometrics 67, 503–514 (2006)CrossRefGoogle Scholar
  22. 22.
    Thijs, B., Glänzel, W.: The influence of author self-citations on bibliometric meso-indicators. The case of European universities. Scientometrics 66, 71–80 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of PassauPassauGermany
  2. 2.University of TwenteEnschedeThe Netherlands

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