Page 178 - JOURNAL OF LIBRARY SCIENCE IN CHINA 2018 Vol. 42

YU Houqiang，Bradley M. Hemminger，XIAO Tingting & QIU Junping / Features of Sina Weibo altmetrics indicator 177

challenge and opportunity for altmetrics. Because content analysis of altmetrics indicators proposes
higher requirement for technical operation and research cycle. On the other hand, altmetrics
indicators involve context data of scholarly artefacts, which happens to be what citation analysis
lacks. Researchers have no way to deduct formation and usage process of citation, while altmetrics
indicators make it possible to study scholarly behavior from perspective of human beings.
In order to make full use of scientific big data to better serve scholarly communication,
collaboration, production and evaluation, both international and local altemtrics databases should
be developed. Scientists are encouraged to use modern scientific communication and cooperation
tools to improve the scientific efficiency. More over, before value of altmetrics indicators is
completely revealed, it is inappropriate to use it for formal scientific evaluation, especially for
scholarly value evaluation. As observed in this study, even the same altmetrics indicator, for
example, Weibo altmetrics indicator, could have multifaceted inner motivation and convey value.
The future research trend is to deconstruct the inside altmetrics indicators, investigate and use in
different categories.

Acknowledgements

The research is supported by China Scholarship Council (No.201506270024). The authors thank
Altmetric LLP for providing the dataset and Dr. Shenmeng Xu for her kind comments.

References

Das, T. (2015). Measuring scholarly use of government information: An altmetrics analysis of federal
statistics. Government Information Quarterly, 32(3), 246-252.
Dawson, S. (2015). New ways to filter online discussion of scientific information. Retrieved Jan. 28, 2015,
from http://www.prnewswire.com/news-releases/new-ways-to-filter-online-discussion-of-scientific-
information-559545291.html.
Friedrich, N., Bowman, T. D., Stock, W. G., & Haustein, S. (2015). Adapting sentiment analysis for tweets
linking to scientific papers. Retrieved Sept. 28, 2015, from https://arxiv.org/abs/1507.01967.
Hammarfelt, B. (2014). Using altmetrics for assessing research impact in the humanities.Scientome-
trics,101(2), 1419-1430.
Haustein, S., Bowman, T. D., Holmberg, K., Peters, I., & Larivière, V. (2014). Astrophysicists on Twitter:
An in-depth analysis of tweeting and scientific publication behavior. Aslib Journal of Information
Management, 66(3), 279-296.
Haustein, S., Bowman, T. D., Macaluso, B., Sugimoto, C. R., & Larivière, V. (2014). Measuring Twitter
activity of arXiv e-prints and published papers. Retrieved Dec. 10, 2015, from https://dx.doi.org/10.6084/
m9.figshare.1041514.v1.
Haustein, S., Peters, I., Sugimoto, C. R., Thelwall, M., & Larivière, V. (2014). Tweeting biomedicine: An
analysis of tweets and citations in the biomedical literature. Journal of the Association for Information