Article’s scientific prestige: Measuring the impact of individual articles in the web of science

Ying Chen, Thorsten Koch, Nazgul Zakiyeva, Kailiang Liu, Zhitong Xu, Chun-houh Chen, Junji Nakano and Keisuke Honda

Journal of Informetrics, 2023, vol. 17, issue 1

Abstract: We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and over a billion citations on 254 subjects from 1981 to 2020. We proposed the Article’s Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.

Keywords: Citation network analysis; Direct citations; Scientific impact; Eigenvector centrality; Citations counts; Cross-subject citations (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:17:y:2023:i:1:s1751157723000044

DOI: 10.1016/j.joi.2023.101379

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