An Overview of Altmetrics Research: A Typology Approach
Abstract
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.
Keywords
Altmetrics research Typology Topic modeling Text miningReferences
- 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.Harley, D.: Scholarly communication: cultural contexts, evolving models. Science 342(6154), 80–82 (2013). https://doi.org/10.1126/science.1243622CrossRefGoogle Scholar
- 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). https://doi.org/10.1002/asi.24162CrossRefGoogle Scholar
- 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). https://doi.org/10.2196/jmir.2012CrossRefGoogle Scholar
- 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). https://doi.org/10.1016/j.joi.2018.06.003CrossRefGoogle Scholar
- 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). https://doi.org/10.1002/asi.20929CrossRefGoogle Scholar
- 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). https://doi.org/10.1002/pra2.2016.14505301077CrossRefGoogle Scholar
- 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). https://doi.org/10.1007/11562214_24CrossRefGoogle Scholar
- 9.Papachristopoulos, L., et al.: Discovering the structure and impact of the digital library evaluation domain. Int. J. Digit. Libr. 20, 125–141 (2017). https://doi.org/10.1007/s00799-017-0222-xCrossRefGoogle Scholar
- 10.Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
- 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.Bungo, J.: Embedded systems programming in the cloud: a novel approach for academia. IEEE Potentials 30(1), 17–23 (2011). https://doi.org/10.1109/mpot.2010.938614CrossRefGoogle Scholar
- 13.Atenstaedt, R.: Word cloud analysis of the BJGP. Br. J. Gen. Pract. 62(596), 148 (2012). https://doi.org/10.3399/bjgp12X630142CrossRefGoogle Scholar
- 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). https://doi.org/10.1145/1998076.1998160
- 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). https://doi.org/10.1007/978-3-319-09785-5_16CrossRefGoogle Scholar
- 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). https://doi.org/10.1007/s11192-014-1264-0CrossRefGoogle Scholar
- 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). https://doi.org/10.1007/978-981-13-1053-9_6CrossRefGoogle Scholar
- 18.Borgman, C.L., Furner, J.: Scholarly communication and bibliometrics. Annu. Rev. Inf. Sci. Technol. 36(1), 2–72 (2002). https://doi.org/10.1002/aris.1440360102CrossRefGoogle Scholar
- 19.Nicholas, D., Rowlands, I.: Social media use in the research workflow. Inf. Serv. Use 31(1–2), 61–83 (2011). https://doi.org/10.3233/isu-2011-0623CrossRefGoogle Scholar
- 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