An Overview of Altmetrics Research: A Typology Approach

  • Han ZhengEmail author
  • Xiaoyu Chen
  • Xu Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11853)


Altmetrics, novel metrics based on social media, have received much attention from scholars in recent years. As an emerging research area in information science, it is essential to understand the overview of altmetrics research. We extracted 731 altmetrics-related articles from the Scopus databases and adopted a text mining method (i.e., topic modeling) to develop a typology of altmetrics research. Six major themes were identified in our analysis, including altmetrics research in general, bibliometric and altmetrics, measuring research impact, metrics application, social media use, and performance evaluation. We interpreted the meaning of the six themes and their associations with altmetrics research. This paper is a first step in mapping the landscape of altmetrics research through uncovering the core topics discussed by scholars. Limitations and future work are also discussed.


Altmetrics research Typology Topic modeling Text mining 


  1. 1.
    Thelwall, M., Haustein, S., Larivière, V., Sugimoto, C.R.: Do altmetrics work? Twitter and ten other social web services. PLoS ONE 8(5), e64841 (2013)CrossRefGoogle Scholar
  2. 2.
    Harley, D.: Scholarly communication: cultural contexts, evolving models. Science 342(6154), 80–82 (2013). Scholar
  3. 3.
    Aung, H.H., Zheng, H., Erdt, M., Aw, A.S., Sin, S.J., Theng, Y.L.: Investigating familiarity and usage of traditional metrics and altmetrics. J. Assoc. Inf. Sci. Technol. (2019). Scholar
  4. 4.
    Eysenbach, G.: Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact. J. Med. Internet Res. 13(4), e123 (2011). Scholar
  5. 5.
    Yu, H., Xu, S., Xiao, T.: Is there Lingua Franca in informal scientific communication? Evidence from language distribution of scientific tweets. J. Inf. 12(3), 605–617 (2018). Scholar
  6. 6.
    Chua, A.Y., Yang, C.C.: The shift towards multi-disciplinarily in information science. J. Am. Soc. Inf. Sci. Technol. 59(13), 2156–2170 (2008). Scholar
  7. 7.
    Pal, A., Chua, A.Y.: Reviewing the landscape of research on the threats to the quality of user-generated content. Proc. Assoc. Inf. Sci. Technol. 53(1), 1–9 (2016). Scholar
  8. 8.
    Yoshida, M., Nakagawa, H.: Automatic term extraction based on perplexity of compound words. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 269–279. Springer, Heidelberg (2005). Scholar
  9. 9.
    Papachristopoulos, L., et al.: Discovering the structure and impact of the digital library evaluation domain. Int. J. Digit. Libr. 20, 125–141 (2017). Scholar
  10. 10.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  11. 11.
    Erdt, M., Nagarajan, A., Sin, S.C.J., Theng, Y.L.: Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media. Scientometrics 109(2), 1117–1166 (2016)CrossRefGoogle Scholar
  12. 12.
    Bungo, J.: Embedded systems programming in the cloud: a novel approach for academia. IEEE Potentials 30(1), 17–23 (2011). Scholar
  13. 13.
    Atenstaedt, R.: Word cloud analysis of the BJGP. Br. J. Gen. Pract. 62(596), 148 (2012). Scholar
  14. 14.
    Noh, Y., Hagedorn, K., Newman, D.: Are learned topics more useful than subject headings. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, pp. 411–412. ACM (2011).
  15. 15.
    Weller, K.: Social media and altmetrics: an overview of current alternative approaches to measuring scholarly impact. In: Welpe, I.M., Wollersheim, J., Ringelhan, S., Osterloh, M. (eds.) Incentives and Performance, pp. 261–276. Springer, Cham (2015). Scholar
  16. 16.
    Zahedi, Z., Costas, R., Wouters, P.: How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications. Scientometrics 101(2), 1491–1513 (2014). Scholar
  17. 17.
    Zheng, H., Erdt, M., Theng, Y.-L.: How do scholars evaluate and promote research outputs? An NTU case study. In: Erdt, M., Sesagiri Raamkumar, A., Rasmussen, E., Theng, Y.-L. (eds.) AROSIM 2018. CCIS, vol. 856, pp. 72–80. Springer, Singapore (2018). Scholar
  18. 18.
    Borgman, C.L., Furner, J.: Scholarly communication and bibliometrics. Annu. Rev. Inf. Sci. Technol. 36(1), 2–72 (2002). Scholar
  19. 19.
    Nicholas, D., Rowlands, I.: Social media use in the research workflow. Inf. Serv. Use 31(1–2), 61–83 (2011). Scholar
  20. 20.
    Zheng, H., Aung, H.H., Erdt, M., Peng, T.Q., Sesagiri Raamkumar, A., Theng, Y.L.: Social media presence of scholarly journals. J. Assoc. Inf. Sci. Technol. 70(3), 256–270 (2019)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Wee Kim Wee School of Communication and InformationNanyang Technological UniversitySingaporeSingapore

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