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Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA

Author

Listed:
  • Carlos G. Figuerola

    (University of Salamanca)

  • Francisco Javier García Marco

    (University of Zaragoza)

  • María Pinto

    (University of Granada)

Abstract

This paper offers an overview of the bibliometric study of the domain of library and information science (LIS), with the aim of giving a multidisciplinary perspective of the topical boundaries and the main areas and research tendencies. Based on a retrospective and selective search, we have obtained the bibliographical references (title and abstract) of academic production on LIS in the database LISA in the period 1978–2014, which runs to 92,705 documents. In the context of the statistical technique of topic modeling, we apply latent Dirichlet allocation, in order to identify the main topics and categories in the corpus of documents analyzed. The quantitative results reveal the existence of 19 important topics, which can be grouped together into four main areas: processes, information technology, library and specific areas of information application.

Suggested Citation

  • Carlos G. Figuerola & Francisco Javier García Marco & María Pinto, 2017. "Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1507-1535, September.
    • Handle: RePEc:spr:scient:v:112:y:2017:i:3:d:10.1007_s11192-017-2432-9
    DOI: 10.1007/s11192-017-2432-9
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    References listed on IDEAS

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    1. Staša Milojević & Cassidy R. Sugimoto & Erjia Yan & Ying Ding, 2011. "The cognitive structure of Library and Information Science: Analysis of article title words," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
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    Cited by:

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    3. Song, Ningyuan & Chen, Kejun & Zhao, Yuehua, 2023. "Understanding writing styles of scientific papers in the IS-LS domain: Evidence from abstracts over the past three decades," Journal of Informetrics, Elsevier, vol. 17(1).

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