Measuring the impact of co-author count on citation count of research publications

IF 1.6 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE COLLNET Journal of Scientometrics and Information Management Pub Date : 2022-01-02 DOI:10.1080/09737766.2021.2016356
Ali Daud, Malik Khizar Hayat, Abdulrahman A. Alshdadi, Ameena T Banjar, W. Alharbi
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Abstract

Practically, co-authored research work reaches higher visibility and impact as compared to the individual published work. The objective of this study is to analyze the correlation between the number of coauthors in a published paper and the number of times that paper is cited in the literature. The analysis is divided into three categories: (i) research field-based analysis; (ii) influential co-author-based analysis and (iii) influential first author-based analysis. The ArnetMiner dataset version 6 is used for analysis. The research methodology is composed of research-field-based, influential co-authors-based, and influential co-author as a first author-based correlational analysis of citations for research articles. The research area is defined for each research article using the abstract from the dataset. The results show that most of the research fields have increasing citability with a greater number of co-authors. Research fields like programming languages carry more citations and knowledge representation and reasoning carry fewer citations with a higher number of co-authors in a paper. With an increased H-index of co-author and first co-author in a paper, the association between co-authors and citations is more negative than positive. However, in the field of bioinformatics, the association is positive both with influential an co-author and first co-author of a paper. This paper fulfils the need to identify role of collaboration in gaining research citability. It enhances the credibility of research both in academia and industry.
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测量合著者数量对研究出版物被引次数的影响
实际上,与个人发表的工作相比,合著的研究工作具有更高的知名度和影响力。本研究的目的是分析已发表论文的合著者数量与该论文在文献中被引用次数之间的相关性。分析分为三类:(一)基于研究领域的分析;(ii)基于有影响力的合著者的分析和(iii)基于有影响的第一作者的分析。ArnetMiner数据集版本6用于分析。研究方法由基于研究领域、基于有影响力的合著者和基于第一作者的有影响力的合作者对研究文章引用的相关性分析组成。使用数据集的摘要为每篇研究文章定义研究领域。研究结果表明,随着合著者数量的增加,大多数研究领域的可引用性都在增加。程序设计语言等研究领域引用次数较多,而知识表示和推理引用次数较少,论文的合著者数量较多。随着论文中合著者和第一位合著者的H指数的增加,合著者和引文之间的联系更多是负面的,而不是正面的。然而,在生物信息学领域,无论是有影响力的论文合著者还是第一位论文合著者,这种联系都是积极的。本文满足了确定合作在获得研究可引用性方面的作用的需要。它提高了学术界和工业界研究的可信度。
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来源期刊
COLLNET Journal of Scientometrics and Information Management
COLLNET Journal of Scientometrics and Information Management INFORMATION SCIENCE & LIBRARY SCIENCE-
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