Preprint Article Version 4 Preserved in Portico This version is not peer-reviewed

A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape

Version 1 : Received: 23 June 2020 / Approved: 24 June 2020 / Online: 24 June 2020 (13:48:32 CEST)
Version 2 : Received: 29 June 2020 / Approved: 30 June 2020 / Online: 30 June 2020 (10:40:09 CEST)
Version 3 : Received: 17 July 2020 / Approved: 20 July 2020 / Online: 20 July 2020 (10:33:46 CEST)
Version 4 : Received: 10 September 2020 / Approved: 12 September 2020 / Online: 12 September 2020 (09:49:40 CEST)

How to cite: Aristovnik, A.; Ravšelj, D.; Umek, L. A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape. Preprints 2020, 2020060299 (doi: 10.20944/preprints202006.0299.v4). Aristovnik, A.; Ravšelj, D.; Umek, L. A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape. Preprints 2020, 2020060299 (doi: 10.20944/preprints202006.0299.v4).

Abstract

The COVID-19 pandemic caused by the novel coronavirus emerged in Wuhan City, Hubei province of China at the end of 2019, has radically transformed the lives of people around the world. Due to its fast spreading, it is currently considered as a worldwide health, social and economic concern. The lack of knowledge on this area has encouraged academic sphere for extensive research, which is reflected in exponentially growing scientific literature in this area. However, current state of COVID-19 research reveals only early development of knowledge, while a comprehensive and in-depth overview remains neglected. Accordingly, the main aim of this paper is to fill the aforementioned gap in the literature and provide an extensive bibliometric analysis of COVID-19 research across science and social science research landscape, using innovative and sophisticated bibliometric approaches (e.g. Venn diagram, Biblioshiny descriptive statistics, VOSviewer co-occurrence network analysis, Jaccard distance cluster analysis, text mining based on logistic regression). The bibliometric analysis is based on the Scopus database including all relevant and latest information on COVID-19 related publications (n=16,866) in the first half of 2020. The empirical results indicate that there is still a lack of publications of COVID-19 and its implications in less-explored (non-health) sciences, especially in social sciences. Accordingly, the findings emphasize an importance of a comprehensive and in-depth approach considering different scientific disciplines in COVID-19 research. The understanding of the evolution of emerging scientific knowledge on COVID-19 is beneficial not only for scientific community but also for evidence-based policymaking in order to prevent and address the COVID-19 pandemic.

Subject Areas

COVID-19; coronavirus; pandemic; science; social science; bibliometric analysis

Comments (1)

Comment 1
Received: 12 September 2020
Commenter: Aleksander Aristovnik
Commenter's Conflict of Interests: Author
Comment: Extended version with text mining based on logistic regression and a cluster analysis based on the Jaccard distance.
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