Advertisement

Scientometrics

, Volume 111, Issue 1, pp 267–283 | Cite as

Context of altmetrics data matters: an investigation of count type and user category

  • Houqiang YuEmail author
Article

Abstract

Context of altmetrics data is essential for further understanding value of altmetrics beyond raw counts. Mainly two facets of context are explored, the count type which reflects user’s multiple altmetrics behaviors and user category which reflects part of user’s background. Based on 5.18 records provided by Altmetric.com, both descriptive statistics and t test result show significant difference between number of posts (NP) and number of unique users (NUU). For several altmetrics indicators, NP has moderate to low correlation with NUU. User category is found to have huge impact on altmetrics count. Analysis of twitter user category shows the general tweet distribution is strongly influenced by the public user. Tweets from research user are more correlated with citations than any other user categories. Moreover, disciplinary difference exists for different user categories.

Keywords

Altmetrics Count type User category Correlation analysis Twitter 

Notes

Acknowledgements

Thank Altmetric.com for providing the dataset and anonymous reviewers for their useful comments. The research is supported by China Scholarship Council (NO: 201506270024) and National Social Science Foundation of China (CTQ023).

References

  1. Altmetric LLP. (2016). How are Twitter demographics determined? https://help.altmetric.com/support/solutions/articles/6000060978-how-are-twitter-demographics.
  2. Andersen, J. P., & Haustein, S. (2015). Influence of study type on Twitter activity for medical research papers. Retrieved from https://arxiv.org/abs/1507.00154.
  3. Bartneck, C., & Kokkelmans, S. (2010). Detecting h-index manipulation through self-citation analysis. Scientometrics, 87(1), 85–98.CrossRefGoogle Scholar
  4. Bornmann, L. (2015a). Alternative metrics in scientometrics: A meta-analysis of research into three altmetrics. Scientometrics, 103(3), 1123–1144.CrossRefGoogle Scholar
  5. Bornmann, L. (2015b). Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS and F1000Prime. Aslib Journal of Information Management, 67(3), 305–319.CrossRefGoogle Scholar
  6. Bornmann, L., & Haunschild, R. (2015). t factor: A metric for measuring impact on Twitter. Retrieved from http://arxiv.org/abs/1508.02179.
  7. Bowman, T. D. (2015). Differences in personal and professional tweets of scholars. Aslib Journal of Information Management, 67(3), 356–371.CrossRefGoogle Scholar
  8. Costas, R., Zahedi, Z., & Wouters, P. (2015). Do “altmetrics” correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective. Journal of the Association for Information Science and Technology, 66(10), 2003–2019.CrossRefGoogle Scholar
  9. Haunschild, R., & Bornmann, L. (2016). Normalization of Mendeley reader counts for impact assessment. Journal of Informetrics, 10(1), 62–73.CrossRefGoogle Scholar
  10. Haustein, S., Bowman, T. D., & Costas, R. (2015). When is an article actually published? An analysis of online availability, publication, and indexation dates. Retrieved from https://arxiv.org/abs/1505.00796.
  11. Haustein, S., Bowman, T. D., Holmberg, K., et al. (2016). Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter. Journal of the Association for Information Science and Technology, 67(1), 232–238.CrossRefGoogle Scholar
  12. Haustein, S., & Costas, R. (2015). Identifying Twitter audiences: Who is tweeting about scientific papers? Retrieved from https://www.asist.org/SIG/SIGMET/wp-content/uploads/2015/10/sigmet2015_paper_11.pdf.
  13. Haustein, S., Costas, R., & Lariviere, V. (2015b). Characterizing social media metrics of scholarly papers: The effect of document properties and collaboration patterns. PLoS ONE, 10(3), e0120495.CrossRefGoogle Scholar
  14. Haustein, S., Peters, I., Sugimoto, C. R., Thelwall, M., et al. (2014). Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature. Journal of the Association for Information Science and Technology, 65(4), 656–669.CrossRefGoogle Scholar
  15. Holmberg, K., & Thelwall, M. (2014). Disciplinary differences in Twitter scholarly communication. Scientometrics, 101(2), 1027–1042.CrossRefGoogle Scholar
  16. Jamali, H. R., & Alimohammadi, D. (2015). Blog citations as indicators of the societal impact of research: Content analysis of social sciences blogs. International Journal of Knowledge Content Development & Technology, 5(1), 15–32.CrossRefGoogle Scholar
  17. Ke, Q., Ahn, Y. Y., & Sugimoto, C. R. (2016). A systematic identification and analysis of scientists on Twitter. Retrieved from https://arxiv.org/abs/1608.06229.
  18. Maleki, A. (2016). Do tweets indicate scholarly communication? Retrieved from http://altmetrics.org/wp-content/uploads/2016/09/altmetrics16_paper_8.pdf.
  19. Mamtora, J., & Haddow, G. (2015). From bibliometrics to altmetrics: An Australian study. Presented at IFLA academic and research libraries satellite conference. In The Quest for deeper meaning of research support, Cape Town, South Africa, 13–14 August, 2015.Google Scholar
  20. NISO. (2016). Altmetrics definitions and use cases. Retrieved from http://www.niso.org/topics/tl/altmetrics_initiative/.
  21. Song, L. P., Chen, W., & He, Y. (2015). Empirical study on article level scientific evaluation—take PLoS ONE as example. Library Work and Study, 233(7), 85–88.Google Scholar
  22. Thelwall, M., Haustein, S., Lariviere, V., & Sugimoto, C. R. (2013). Do altmetrics work? Twitter and ten other social web services. PLoS ONE, 8(5), e64841.CrossRefGoogle Scholar
  23. Yu, H. Q., Hemminger, B. M., Qiu, J. P., & Xiao, T. T. (2016). Study on characteristics of sina weibo altmetrics. Journal of Library Science in China, 42(4), 20–36.Google Scholar
  24. Zahedi, Z., Costas, R., & Wouters, P. (2014). How well developed are altmetrics? A cross-disciplinary analysis of the presence of alternative metrics’ in scientific publications. Scientometrics, 101(2), 1491–1513.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.School of Information ManagementWuhan UniversityWuhanChina
  2. 2.Research Center for China Science EvaluationWuhan UniversityWuhanChina

Personalised recommendations