This article provides an introduction to the use of altmetrics as a tool to assess research impact. In particular, it looks at the evidence behind claims that altmetrics allow the impact of research to be measured in days rather than years. Low correlations between altmetrics and article citations make it doubtful that altmetrics can reliably predict future citations. In addition, there are good reasons to qualify statements that altmetrics measure the wider impact of research on society. Librarians should be careful not to overstate the value of altmetrics when recommending their use as a complement to more traditional measures of research quality.
This paper presents a review of altmetrics or alternative metrics. This concept is defined as the creation and study of new indicators for analysing scientific and academic research activity based onWeb 2.0. The underlying premise is that variables such as mentions in blogs, numberof tweets or saves ofan articleby researchersin reference management systems, may be a valid measure of the use and impactof scientific publications. In this respect,these measuresare becoming particularly relevant, being at the centre of debate within the bibliometric community. Firstly,an explanation is given of the main platforms and indicators for this type of measurement. Subsequently,a study is undertaken of a selection of papers from the field of communication, comparing the number of citations received withtheir 2.0 indicators.The results show that the most cited articles within recent years also have significantly higher altmetric indicators. Next follows a review of the principal empirical studies undertaken, centering on the correlations between bibliometric and alternative indicators. To conclude, the main limitations of altmetrics are highlighted,alongside a reflective consideration of the role altmetrics may play in capturing the impactof research in Web 2.0 platforms.
H. Nguyen, D. Bozhkov, Z. Ahmadi, N. Nguyen, and T. Doan. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, (July 2022)
N. Weibel, L. Cowan, L. Pina, W. Griswold, and J. Hollan. Adjunct Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology, page 405--406. New York, NY, USA, ACM, (2010)Poster.
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017), Eindhoven, The Netherlands, Eindhoven University of Technology, (2017)
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017), Eindhoven, The Netherlands, Eindhoven University of Technology, (2017)
M. Atzmueller. Proc. ECML/PKDD 2014: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, volume 8726 of LNCS, page 485--488. Heidelberg, Germany, Springer Verlag, (2014)
M. Atzmueller, S. Bobek, M. Kibanov, and G. Nalepa. Proceedings of the 2014 International Smart University Workshop (SmartU 2014), London, UK, London, UK, (2014)
Gunjal, and Bhojaraju. Proceedings of International Conference on What Next in Libraries (WNL-2K15), January 21-23, Silchar, NIT Silchar, Assam, (2015)
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017), Eindhoven, The Netherlands, Eindhoven University of Technology, (2017)