The purpose of this case study is to investigate the usefulness of altmetrics for measuring the broader impact of research.
This case study is based on a sample of 1,082 the Public Library of Science (PLOS) journal articles recommended in F1000. The data set includes altmetrics which were provided by PLOS. The F1000 data set contains tags on papers which were assigned by experts to characterise them.
The most relevant tag for altmetric research is “good for teaching”, as it is assigned to papers which could be of interest to a wider circle of readers than the peers in a specialised area. One could expect papers with this tag to be mentioned more often on Facebook and Twitter than those without this tag. The results from regression models were able to confirm these expectations: papers with this tag show significantly higher Facebook and Twitter counts than papers without this tag. This clear association could not be seen with Mendeley or Figshare counts (that is with counts from platforms which are chiefly of interest in a scientific context).
The results of the current study indicate that Facebook and Twitter, but not Figshare or Mendeley, might provide an indication of which papers are of interest to a broader circle of readers (and not only for the peers in a specialist area), and could therefore be useful for the measurement of the societal impact of research.
The author would like to thank Adie Chan, Ros Dignon, and Iain Hrynaszkiewicz from F1000 for providing the author with the F1000Prime data set.
Bornmann, L. (2015), "Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS and F1000Prime", Aslib Journal of Information Management, Vol. 67 No. 3, pp. 305-319. https://doi.org/10.1108/AJIM-09-2014-0115
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