, Volume 104, Issue 1, pp 1–18 | Cite as

How is an academic social site populated? A demographic study of Google Scholar Citations population



This paper intends to describe the population evolution of a scientific information web service during 2011–2012. Quarterly samples from December 2011 to December 2012 were extracted from Google Scholar Citations to analyse the number of members, distribution of their bibliometric indicators, positions, institutional and country affiliations and the labels to describe their scientific activity. Results show that most of the users are young researchers, with a starting scientific career and mainly from disciplines related to information sciences and technologies. Another important result is that this service is settled by waves emanating from specific institutions and countries. This work concludes that this academic social network presents some biases in the population distribution that does not make it representative of the real scientific population.


Web bibliometrics Google Scholar Citations Academic social networks Web demography 



I would like to thank Jennifer Carranza her helpful recommendations on the English version of this paper and the interesting suggestions of the reviewers.


  1. Aguillo, I. F. (2012). Is Google Scholar useful for bibliometrics? A webometric analysis. Scientometrics, 91(2), 343–351.CrossRefGoogle Scholar
  2. Almousa, O. (2011). Users’ classification and usage-pattern identification in academic social networks. In IEEE Jordan conference on applied electrical engineering and computing technologies AEECT (pp 1–6). New York: IEEE.Google Scholar
  3. Bakkalbasi, N., Bauer, K., Glover, J., & Wang, L. (2006). Three options for citation tracking: Google Scholar, Scopus and Web of Science. Biomedical Digital Libraries, 3, 7.
  4. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230.CrossRefGoogle Scholar
  5. Chakraborty, N. (2012). Activities and reasons for using social networking sites by research Scholars in NEHU: A study on Facebook and ResearchGate. 8th convention PLANNER-2012, Sikkim University, Gangtok. Ahmedabad, IN: IFLIBNET. Retrieved from
  6. Chang, J., Rosenn, I., Backstrom, L., & Marlow, C. (2010). ePluribus: Ethnicity on social networks. In Fourth international conference on weblogs and social media (ICWSM-10). Washington DC: AAAI Press.Google Scholar
  7. Delgado López-Cózar, E., Robinson-García, N., & Torres-Salinas, D. (2014). The Google scholar experiment: How to index false papers and manipulate bibliometric indicators. Journal of the Association for Information Science and Technology, 65(3), 446–454.CrossRefGoogle Scholar
  8. Duggan, M., & Smith, A. (2013). Social media update 2013. Washington DC: Pew Research Center. Retrieved from
  9. Ebner, M., & Reinhardt, W. (2009). Social networking in scientific conferences—Twitter as tool for strengthen a scientific community. In 4th European conference on technology enhanced learning, EC-TEL 2009. Nice: Springer.Google Scholar
  10. Eysenbach, G. (2011). Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact. Journal of Medical Internet Research, 13(4), e123.CrossRefGoogle Scholar
  11. Garcia, D., Mavrodiev, P., & Schweitzer, F. (2013). Social resilience in online communities: The autopsy of Friendster. Retrieved from
  12. Glänzel, W., & Heeffer, S. (2014). Cross-national preferences and similarities in downloads and citations of scientific articles: A pilot study. In Proceedings of the science and technology indicators conference. Leiden: Universiteit Leiden.Google Scholar
  13. Google Refine. (2015). Google Refine, a power tool for working with messy data (formerly Freebase Gridworks): Google Project Hosting.
  14. Halevi, G., & Moed, H. (2014). Usage patterns of scientific journals and their relationship with citations. In Proceedings of the science and technology indicators conference. Leiden: Universiteit Leiden.Google Scholar
  15. Haley, M. R. (2014). Ranking top economics and finance journals using Microsoft academic search versus Google scholar: How does the new publish or perish option compare? Journal of the Association for Information Science and Technology, 65(5), 1079–1084.Google Scholar
  16. Haustein, S., Peters, I., Bar-Ilan, J., Priem, J., Shema, H., & Terliesner, J. (2014). Coverage and adoption of altmetrics sources in the bibliometric community. Scientometrics. doi: 10.1007/s11192-013-1221-3.MATHGoogle Scholar
  17. Hogan, N. M., & Sweeney, K. J. (2013). Social networking and scientific communication: A paradoxical return to Mertonian roots? Journal of the American Society for Information Science and Technology, 64(3), 644–646.CrossRefGoogle Scholar
  18. Huang, Z., & Yuan, B. (2012). Mining Google Scholar Citations: An exploratory study. Lecture Notes in Computer Science, 7389, 182–189.Google Scholar
  19. Jacsó, P. (2008). Google Scholar revisited. Online Information Review, 32(1), 102–114.CrossRefGoogle Scholar
  20. Kousha, K., & Thelwall, M. (2007). Google Scholar Citations and Google Web-URL citations: A multi-discipline exploratory analysis. Journal of the American Society for Information Science and Technology, 58(7), 1055–1065.CrossRefGoogle Scholar
  21. Li, X., Thelwall, M., & Giustini, D. (2012). Validating online reference managers for scholarly impact measurement. Scientometrics, 91(2), 461–471.CrossRefGoogle Scholar
  22. Mas-Bleda, A., Thelwall, M., Kousha, K., & Aguillo, I. F. (2014). Do highly cited researchers successfully use the social web? Scientometrics,. doi: 10.1007/s11192-014-1345-0.Google Scholar
  23. Meho, L. I., & Yang, K. (2007). Impact of data sources on citation counts and rankings of LIS faculty: Web of science versus Scopus and Google scholar. Journal of the American Society for Information Science and Technology, 58(13), 2105–2125.CrossRefGoogle Scholar
  24. Mendeley. (2012). Global research report. Retrieved from
  25. Menendez, M., de Angeli, A., & Menestrina, Z. (2012). Exploring the virtual space of academia. In J. Dugdale, et al. (Eds.), From research to practice in the design of cooperative systems: Results and open challenges. London: Springer.Google Scholar
  26. Milojević, S. (2010). Power law distributions in information science: Making the case for logarithmic binning. Journal of the American Society for Information Science and Technology, 61(12), 2417–2425.CrossRefGoogle Scholar
  27. Mislove, A., Lehmann, S., Ahn, Y. Y., Onnela, J. P., & Rosenquist, J. N. (2011). Understanding the demographics of Twitter users. In 5th international AAAI conference on weblogs and social media (pp 554–557). Barcelona: AAAI Press.Google Scholar
  28. Moed, H. F. (2005). Statistical relationships between downloads and citations at the level of individual documents within a single journal. Journal of the American Society for Information Science and Technology, 56(10), 1088–1097.CrossRefGoogle Scholar
  29. Ortega, J. L. (2014). Academic search engines: A quantitative outlook (p 200). Cambridge: Chandos Publishing. ISBN:1843347911.Google Scholar
  30. Ortega, J. L., & Aguillo, I. F. (2012). Science is all in the eye of the beholder: Keyword maps in Google Scholar Citations. Journal of the American Society for Information Science and Technology, 63(12), 2370–2377.CrossRefGoogle Scholar
  31. Ortega, J. L., & Aguillo, I. F. (2013). Institutional and country collaboration in an online service of scientific profiles: Google Scholar Citations. Journal of Informetrics, 7(2), 394–403.CrossRefGoogle Scholar
  32. Ortega, J. L., & Aguillo, I. F. (2014). Microsoft academic search and Google scholar citations: A comparative analysis of author profiles. Journal of the American Society for Information Science and Technology, 65(6), 1149–1156.Google Scholar
  33. Pitney, W. A., & Gilson, T. A. (2012). Educational technology: Using Google Scholar Citations to support the impact of scholarly work. Athletic Training Education Journal, 7(1), 38–39.CrossRefGoogle Scholar
  34. Radicchi, F., & Castellano, C. (2013). Analysis of bibliometric indicators for individual scholars in a large data set. Scientometrics, 97(3), 627–637.CrossRefGoogle Scholar
  35. ResearchGate. (2014). Main page. Retrieved from
  36. Seber, G. A. F. (2002). The estimation of animal abundance and related parameters. Caldwel, NJ: Blackburn Press.Google Scholar
  37. Shneiderman, B. (2008). Science 2.0. Science, 319(5868), 1349–1350.CrossRefGoogle Scholar
  38. Thelwall, M., & Kousha, K. (2014). Social network or academic network? Journal of the Association for Information Science and Technology, 65(4), 721–731.CrossRefGoogle Scholar
  39. Tilling, K. (2001). Capture–recapture methods—Useful or misleading? International Journal of Epidemiology, 30(1), 12–14.CrossRefGoogle Scholar
  40. Van Eperen, L., & Marincola, F. M. (2011). How scientists use social media to communicate their research. Journal of Translational Medicine, 9(1), 1–3.CrossRefGoogle Scholar
  41. Watson, A. B. (2009). Comparing citations and downloads for individual articles at the Journal of Vision. Journal of Vision, 9(4), article i. Retrieved from

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2015

Authors and Affiliations

  1. 1.Cybermetrics LabCCHS-CSICMadridSpain

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