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Combining dissimilarity measures for quantifying changes in research fields

Author

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  • Lukun Zheng

    (Western Kentucky University)

  • Yuhang Jiang

    (Western Kentucky University)

Abstract

The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods.

Suggested Citation

  • Lukun Zheng & Yuhang Jiang, 2022. "Combining dissimilarity measures for quantifying changes in research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3751-3765, July.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:7:d:10.1007_s11192-022-04415-5
    DOI: 10.1007/s11192-022-04415-5
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    References listed on IDEAS

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    1. Finardi, Ugo, 2014. "On the time evolution of received citations, in different scientific fields: An empirical study," Journal of Informetrics, Elsevier, vol. 8(1), pages 13-24.
    2. Jian Xu & Yi Bu & Ying Ding & Sinan Yang & Hongli Zhang & Chen Yu & Lin Sun, 2018. "Understanding the formation of interdisciplinary research from the perspective of keyword evolution: a case study on joint attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 973-995, November.
    3. Xuning Tang & Christopher C. Yang & Min Song, 2013. "Understanding the evolution of multiple scientific research domains using a content and network approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(5), pages 1065-1075, May.
    4. Chen, Baitong & Tsutsui, Satoshi & Ding, Ying & Ma, Feicheng, 2017. "Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 11(4), pages 1175-1189.
    5. Xuning Tang & Christopher C. Yang & Min Song, 2013. "Understanding the evolution of multiple scientific research domains using a content and network approach," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(5), pages 1065-1075, May.
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