Scientometrics

, Volume 111, Issue 3, pp 1801–1812 | Cite as

Assessing author self-citation as a mechanism of relevant knowledge diffusion

Article

Abstract

Author self-citation is a practice that has been historically surrounded by controversy. Although the prevalence of self-citations in different scientific fields has been thoroughly analysed, there is a lack of large scale quantitative research focusing on its usefulness at guiding readers in finding new relevant scientific knowledge. In this work we empirically address this issue. Using as our main corpus the entire set of PLOS journals research articles, we train a topic discovery model able to capture semantic dissimilarity between pairs of articles. By dividing pairs of articles involved in intra-PLOS citations into self-citations (articles linked by a cite which share at least one author) and non-self-citations (articles linked by a cite which share no author), we observe the distribution of semantic dissimilarity between citing and cited papers in both groups. We find that the typical semantic distance between articles involved in self-citations is significantly smaller than the observed one for articles involved in non-self-citations. Additionally, we find that our results are not driven by the fact that authors tend to specialize in particular areas of research, make use of specific research methodologies or simply have particular styles of writing. Overall, assuming shared content as an indicator of relevance and pertinence of citations, our results indicate that self-citations are, in general, useful as a mechanism of knowledge diffusion.

Keywords

Author self-citation Latent Dirichlet allocation Semantic dissimilarity Knowledge diffusion 

References

  1. Aksnes, D. (2003). A macro study of self-citation. Scientometrics, 56(2), 235–246.CrossRefGoogle Scholar
  2. Anauati, V., Galiani, S., & Gálvez, R. H. (2016). Quantifying the life cycle of scholarly articles across fields of economic research. Economic Inquiry, 54(2), 1339–1355.CrossRefGoogle Scholar
  3. Ball, P. (2005). Index aims for fair ranking of scientists. Nature, 436(7053), 900–900.CrossRefGoogle Scholar
  4. Bartneck, C., & Kokkelmans, S. (2011). Detecting h-index manipulation through self-citation analysis. Scientometrics, 87(1), 85–98.CrossRefGoogle Scholar
  5. Bird, S. (2006). Nltk: the natural language toolkit. In Proceedings of the COLING/ACL on interactive presentation sessions, COLING-ACL ’06 (pp. 69–72). Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
  6. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.MATHGoogle Scholar
  7. Bornmann, L., & Daniel, H.-D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.CrossRefGoogle Scholar
  8. Briët, J., & Harremoës, P. (2009). Properties of classical and quantum Jensen–Shannon divergence. Physical Review A, 79(5), 052311.CrossRefGoogle Scholar
  9. Chamberlain, S., Boettiger, C., & Ram, K. (2015). rplos: Interface to the Search ‘API’ for ‘PLoS’ Journals. R package version 0.5.4.Google Scholar
  10. Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., et al. (2015). Psychological language on twitter predicts county-level heart disease mortality. Psychological Science, 26(2), 159–169.CrossRefGoogle Scholar
  11. Engqvist, L., & Frommen, J. (2008). The h-index and self-citations. Proceedings of the National academy of Sciences of the United States of America, 99, 11270–11274.Google Scholar
  12. Estabrooks, C. A., Derksen, L., Winther, C., Lavis, J. N., Scott, S. D., Wallin, L., et al. (2008). The intellectual structure and substance of the knowledge utilization field: A longitudinal author co-citation analysis, 1945 to 2004. Implementation Science, 3(1), 49.CrossRefGoogle Scholar
  13. Fagerberg, J., Srholec, M., & Verspagen, B. (2010). Chapter 20—innovation and economic development. In B. H. Hall & N. Rosenberg (Eds.), Handbook of the economics of innovation (Vol. 2, pp. 833–872). Amsterdam: North-Holland.Google Scholar
  14. Glänzel, W., & Thijs, B. (2004). The influence of author self-citations on bibliometric macro indicators. Scientometrics, 59(3), 281–310.CrossRefGoogle Scholar
  15. Hall, D., Jurafsky, D., & Manning, C. D. (2008). Studying the history of ideas using topic models. In Proceedings of the conference on empirical methods in natural language processing, EMNLP ’08 (pp. 363–371). Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
  16. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National academy of Sciences of the United States of America, 102(46), 16569–16572.CrossRefMATHGoogle Scholar
  17. Hu, D. J. (2009). Latent dirichlet allocation for text, images, and music. http://cseweb.ucsd.edu/~dhu/docs/research_exam09.pdf. Last checked on January 16, 2017.
  18. Hudson, J. (2007). Be known by the company you keep: Citations–quality or chance? Scientometrics, 71(2), 231–238.CrossRefGoogle Scholar
  19. Hyland, K. (2003). Self-citation and self-reference: Credibility and promotion in academic publication. Journal of the American Society for Information Science and Technology, 54(3), 251–259.CrossRefGoogle Scholar
  20. Knorr-Cetina, K. (1981). The manufacture of knowledge. An essay on the constructivist and contextual nature of science. Oxford: Pergamon Press.Google Scholar
  21. Kulkarni, A. V., Aziz, B., Shams, I., & Busse, J. W. (2011). Author self-citation in the general medicine literature. PLoS ONE, 6(6), e20885.CrossRefGoogle Scholar
  22. Lawani, S. M. (1982). On the heterogeneity and classification of author self-citations. Journal of the American Society for Information Science, 33(5), 281.CrossRefGoogle Scholar
  23. MacRoberts, M. H., & MacRoberts, B. R. (1989). Problems of citation analysis: A critical review. Journal of the American Society for Information Science, 40(5), 342–349.CrossRefGoogle Scholar
  24. Maliniak, D., Powers, R., & Walter, B. F. (2013). The gender citation gap in international relations. International Organization, 67(4), 889–922.CrossRefGoogle Scholar
  25. Merton, R. K. (1973). Sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.Google Scholar
  26. Motamed, M., Mehta, D., Basavaraj, S., & Fuad, F. (2002). Self citations and impact factors in otolaryngology journals. Clinical Otolaryngology & Allied Sciences, 27(5), 318–320.CrossRefGoogle Scholar
  27. Park, H. W., Hong, H. D., & Leydesdorff, L. (2005). A comparison of the knowledge-based innovation systems in the economies of South Korea and the Netherlands using Triple Helix indicators. Scientometrics, 65(1), 3–27.CrossRefGoogle Scholar
  28. Public Library of Science. (2015). Plos subject area thesaurus. https://github.com/PLOS/plos-thesaurus. Last checked on January 16, 2017.
  29. Řehůřek, R., & Sojka, P., (2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks (pp. 45–50). Valletta: ELRA.Google Scholar
  30. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.Google Scholar
  31. Schreiber, M. (2007). Self-citation corrections for the Hirsch index. Europhysics Letters, 78(3), 30002.CrossRefGoogle Scholar
  32. Seglen, P. O. (1992). The skewness of science. Journal of the American Society for Information Science, 43(9), 628–638.CrossRefGoogle Scholar
  33. Snyder, H., & Bonzi, S. (1998). Patterns of self-citation across disciplines (1980–1989). Journal of Information Science, 24(6), 431–435.CrossRefGoogle Scholar
  34. Loria, S., Keen, P., Honnibal, M., Yankovsky, R., Karesh, D., Dempsey, E., et al. (2013). TextBlob: Simplified text processing. https://textblob.readthedocs.io/en/dev/. Last checked on January 16, 2017.
  35. Tagliacozzo, R. (1977). Self-citations in scientific literature. Journal of Documentation, 33(4), 251–265.CrossRefGoogle Scholar
  36. Wojick, D. E., Warnick, W. L., Carroll, B. C., & Crowe, J. (2006). The digital road to scientific knowledge diffusion. D-Lib Magazine, 12(6), 1082–9873.CrossRefGoogle Scholar
  37. Yu, G., Wang, M.-Y., & Yu, D.-R. (2010). Characterizing knowledge diffusion of nanoscience & nanotechnology by citation analysis. Scientometrics, 84(1), 81–97.MathSciNetCrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.Departamento de Computación, FCEyNUniversidad de Buenos AiresBuenos AiresArgentina

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