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The impact of author-selected keywords on citation counts

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  • Uddin, Shahadat
  • Khan, Arif

Abstract

A number of bibliometric studies have shown that many factors impact citation counts besides the scientific quality. This paper used a large bibliometric dataset to investigate the impact of the different statistical properties of author-selected keywords and the network attributes of their co-occurrence networks on citation counts. Four statistical properties of author-selected keywords were considered: (i) Keyword growth (i.e., the relative increase or decrease in the presence statistics of an underlying keyword over a given period of time); (ii) Keyword diversity (i.e., the level of variety in a set of author-selected keywords); (iii) Number of keywords; and (iv) Percentage of new keywords. This study also considered network centrality which is a network attribute from the keyword co-occurrence network. Network centrality was calculated using the average of three basic network centrality measures: degree, closeness and betweenness centrality. A correlation and regression analysis showed that all of these factors had a significant positive relation with citation counts except the percentage of new keywords that had a significant negative relation. However, when the effect of four potential control variables (i.e., the number of article authors, the length of an article, the quality of the journal in which the article was published and the length of the title of an article) were controlled, only four variables related to author-selected keywords showed a significant relation with citation counts. Keyword growth, number of keywords and network centrality showed a positive relation with citation counts; whereas, the percentage of new keywords showed a negative relation with citation counts. The implications of these findings are discussed in this article.

Suggested Citation

  • Uddin, Shahadat & Khan, Arif, 2016. "The impact of author-selected keywords on citation counts," Journal of Informetrics, Elsevier, vol. 10(4), pages 1166-1177.
  • Handle: RePEc:eee:infome:v:10:y:2016:i:4:p:1166-1177
    DOI: 10.1016/j.joi.2016.10.004
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    References listed on IDEAS

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    Cited by:

    1. Jungwon Yoon & Han Woo Park, 2020. "Pattern and trend of scientific knowledge production in North Korea by a semantic network analysis of papers in journal titled technological innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1421-1438, August.
    2. Hu, Ya-Han & Tai, Chun-Tien & Liu, Kang Ernest & Cai, Cheng-Fang, 2020. "Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity," Journal of Informetrics, Elsevier, vol. 14(1).
    3. Lu, Chao & Bu, Yi & Dong, Xianlei & Wang, Jie & Ding, Ying & Larivière, Vincent & Sugimoto, Cassidy R. & Paul, Logan & Zhang, Chengzhi, 2019. "Analyzing linguistic complexity and scientific impact," Journal of Informetrics, Elsevier, vol. 13(3), pages 817-829.
    4. Shahadat Uddin & Nazim Choudhury & Md Ekramul Hossain, 2019. "A research framework to explore knowledge evolution and scholarly quantification of collaborative research," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 789-803, May.
    5. Luis-Millán González & Xavier García-Massó & Alberto Pardo-Ibañez & Fernanda Peset & José Devís-Devís, 2018. "An author keyword analysis for mapping Sport Sciences," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-22, August.
    6. Andrea Fronzetti Colladon & Ciriaco Andrea D’Angelo & Peter A. Gloor, 2020. "Predicting the future success of scientific publications through social network and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 357-377, July.
    7. Matheus Becker Costa & Leonardo Moraes Aguiar Lima Santos & Jones Luís Schaefer & Ismael Cristofer Baierle & Elpidio Oscar Benitez Nara, 2019. "Industry 4.0 technologies basic network identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 977-994, November.
    8. Yufeng Duan & Zequan Xiong, 2017. "Download patterns of journal papers and their influencing factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1761-1775, September.
    9. Jungwon Yoon & Han Woo Park, 0. "Pattern and trend of scientific knowledge production in North Korea by a semantic network analysis of papers in journal titled technological innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 0, pages 1-18.
    10. Bornmann, Lutz & Tekles, Alexander & Zhang, Helena H. & Ye, Fred Y., 2019. "Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data," Journal of Informetrics, Elsevier, vol. 13(4).

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