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Mapping of science by combined co‐citation and word analysis. II: Dynamical aspects

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  • Robert R. Braam
  • Henk F. Moed
  • Anthony F. J. van Raan

Abstract

Combined analysis of co‐citation relations and words is explored to study time‐dependent (“dynamical”) aspects of scientific activities, as expressed in research publications. This approach, using words originating from publications citing documents in co‐citation clusters, offers an additional and complementary possibility to identify and link specialty literature through time, compared to the exclusive use of citations. Analysis of co‐citation relations is used to locate and link groups of publications that share a consensus concerning intellectual base literature. Analysis of word‐profile similarity is used to identify and link publication groups that belong to the same subject‐matter research specialty. Different types of “content‐words” are analyzed, including indexing terms, classification codes, and words from title and abstract of publications. The developed methods and techniques are illustrated using data of a specialty in atomic and molecular physics. For this specialty, it is shown that, over a period of 10 years, continuity in intellectual base was at a lower level than continuity in topics of current research. This finding indicates that a series of interesting new contributions are made in course of time, without vast alteration in general topics of research. However, within this framework, a more detailed analysis based on timeplots of individual cited key‐articles and of content‐words reveals a change from more rapid succession of new empirical studies to more retrospective, and theoretically oriented studies in later years. © 1991 John Wiley & Sons, Inc.

Suggested Citation

  • Robert R. Braam & Henk F. Moed & Anthony F. J. van Raan, 1991. "Mapping of science by combined co‐citation and word analysis. II: Dynamical aspects," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 42(4), pages 252-266, May.
    • Handle: RePEc:bla:jamest:v:42:y:1991:i:4:p:252-266
    DOI: 10.1002/(SICI)1097-4571(199105)42:43.0.CO;2-G
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    9. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    10. Bettencourt, Luís M.A. & Kaiser, David I. & Kaur, Jasleen, 2009. "Scientific discovery and topological transitions in collaboration networks," Journal of Informetrics, Elsevier, vol. 3(3), pages 210-221.
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    14. Xie, Qing & Zhang, Xinyuan & Song, Min, 2021. "A network embedding-based scholar assessment indicator considering four facets: Research topic, author credit allocation, field-normalized journal impact, and published time," Journal of Informetrics, Elsevier, vol. 15(4).
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    16. Trappey, Amy J.C. & Wei, Ann Y.E. & Chen, Neil K.T. & Li, Kuo-An & Hung, L.P. & Trappey, Charles V., 2023. "Patent landscape and key technology interaction roadmap using graph convolutional network – Case of mobile communication technologies beyond 5G," Journal of Informetrics, Elsevier, vol. 17(1).
    17. David Chavalarias & Jean-Philippe Cointet, 2008. "Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(1), pages 37-50, April.
    18. Xiaowei Ma & Mei Wang & Chuandong Li, 2019. "A Summary on Research of Household Energy Consumption: A Bibliometric Analysis," Sustainability, MDPI, vol. 12(1), pages 1-17, December.
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