IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v127y2022i7d10.1007_s11192-022-04415-5.html
   My bibliography  Save this article

Combining dissimilarity measures for quantifying changes in research fields

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-022-04415-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-022-04415-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Sixin & Mei, Jun & Xia, Dan & Yang, Zhanying & Hu, Junhao, 2022. "Finite-time optimal feedback control mechanism for knowledge transmission in complex networks via model predictive control," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Lea F. Stöber & Marius Boesino & Andreas Pyka & Franziska Schuenemann, 2023. "Bioeconomy Innovation Networks in Urban Regions: The Case of Stuttgart," Land, MDPI, vol. 12(4), pages 1-22, April.
    3. Seyyed Reza Taher Harikandeh & Sadegh Aliakbary & Soroush Taheri, 2023. "An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1567-1582, March.
    4. Jian Xu & Ying Ding & Yi Bu & Shuqing Deng & Chen Yu & Yimin Zou & Andrew Madden, 2019. "Interdisciplinary scholarly communication: an exploratory study for the field of joint attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1597-1619, June.
    5. 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.
    6. Yating Li & Ye Chen & Qiyu Wang, 2021. "Evolution and diffusion of information literacy topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4195-4224, May.
    7. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
    8. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    9. Zhentao Liang & Jin Mao & Kun Lu & Gang Li, 2021. "Finding citations for PubMed: a large-scale comparison between five freely available bibliographic data sources," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9519-9542, December.
    10. Aaron Lercher & Lawrence Smolinsky, 2016. "Persistent value of older scientific journal articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1205-1220, September.
    11. Huixin Wang & Jing Xie & Shixian Luo & Duy Thong Ta & Qian Wang & Jiao Zhang & Daer Su & Katsunori Furuya, 2023. "Exploring the Interplay between Landscape Planning and Human Well-Being: A Scientometric Review," Land, MDPI, vol. 12(7), pages 1-24, June.
    12. Mingkun Wei & Abdolreza Noroozi Chakoli, 2020. "Evaluating the relationship between the academic and social impact of open access books based on citation behaviors and social media attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2401-2420, December.
    13. Gabriele Sampagnaro, 2023. "Keyword occurrences and journal specialization," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5629-5645, October.
    14. Mauricio Marrone, 2020. "Application of entity linking to identify research fronts and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 357-379, January.
    15. Ugo Finardi, 2017. "Long time series of highly cited articles: an empirical study," IRCrES Working Paper 201712, CNR-IRCrES Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY - former Institute for Economic Research on Firms and Growth - Torino (TO) ITALY.
    16. Sandeep Soni & Kristina Lerman & Jacob Eisenstein, 2021. "Follow the leader: Documents on the leading edge of semantic change get more citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 478-492, April.
    17. Yang, Jinqing & Bu, Yi & Lu, Wei & Huang, Yong & Hu, Jiming & Huang, Shengzhi & Zhang, Li, 2022. "Identifying keyword sleeping beauties: A perspective on the knowledge diffusion process," Journal of Informetrics, Elsevier, vol. 16(1).
    18. Cristina López-Duarte & Marta M. Vidal-Suárez & Belén González-Díaz, 2019. "Cross-national distance and international business: an analysis of the most influential recent models," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 173-208, October.
    19. 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).
    20. Zhao, Yi & Liu, Lifan & Zhang, Chengzhi, 2022. "Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:127:y:2022:i:7:d:10.1007_s11192-022-04415-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.