Analysing self-citations in a large bibliometric database

Citation metrics have value because they aim to make scientific assessment a level playing field, but urgent transparency-based adjustments are necessary to ensure that measurements yield the most accurate picture of impact and excellence. One problematic area is the handling of self-citations, which are either excluded or inappropriately accounted for when using bibliometric indicators for research evaluation. In this talk, in favour of openly tracking self-citations, I report on a study of self-referencing behaviour among various academic disciplines as captured by the curated bibliometric database Web of Science. Specifically, I examine the behaviour of thousands of authors grouped into 15 subject areas like Biology, Chemistry, Science and Technology, Engineering, and Physics. In this talk, I focus on the methodological set-up of the study and discuss data science related problems like author name disambiguation and bibliometric indicator modelling.

This talk bases on the following publication: Kacem, A., Flatt, J. W., & Mayr, P. (2020). Tracking self-citations in academic publishing. Scientometrics, 123(2), 1157–1165. https://doi.org/10.1007/s11192-020-03413-9

Talk given at the "Challenges of scholarly communication: bibliometric transparency and impact" webinar of the UK Chapter of the International Society for Knowledge Organisation, May 25, 2022.