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Evolution of research topics in LIS between 1996 and 2019: an analysis based on latent Dirichlet allocation topic model

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  • Xiaoyao Han

    (Humboldt Universität zu Berlin)

Abstract

This study investigated the evolution of library and information science (LIS) by analyzing research topics in LIS journal articles. The analysis is divided into five periods covering the years 1996–2019. Latent Dirichlet allocation modeling was used to identify underlying topics based on 14,035 documents. An improved data-selection method was devised in order to generate a dynamic journal list that included influential journals for each period. Results indicate that (a) library science has become less prevalent over time, as there are no top topic clusters relevant to library issues since the period 2000–2005; (b) bibliometrics, especially citation analysis, is highly stable across periods, as reflected by the stable subclusters and consistent keywords; and (c) information retrieval has consistently been the dominant domain with interests gradually shifting to model-based text processing. Information seeking and behavior is also a stable field that tends to be dispersed among various topics rather than presented as its own subject. Information systems and organizational activities have been continuously discussed and have developed a closer relationship with e-commerce. Topics that occurred only once have undergone a change of technological context from the networks and Internet to social media and mobile applications.

Suggested Citation

  • Xiaoyao Han, 2020. "Evolution of research topics in LIS between 1996 and 2019: an analysis based on latent Dirichlet allocation topic model," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2561-2595, December.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03721-0
    DOI: 10.1007/s11192-020-03721-0
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    References listed on IDEAS

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    Cited by:

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    2. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    3. 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.
    4. Ivan Heibi & Silvio Peroni, 2021. "A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.'s case," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8433-8470, October.
    5. A. Velez-Estevez & P. García-Sánchez & J. A. Moral-Munoz & M. J. Cobo, 2022. "Why do papers from international collaborations get more citations? A bibliometric analysis of Library and Information Science papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7517-7555, December.
    6. Pertti Vakkari & Yu‐Wei Chang & Kalervo Järvelin, 2022. "Disciplinary contributions to research topics and methodology in Library and Information Science—Leading to fragmentation?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1706-1722, December.
    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. Pertti Vakkari & Yu-Wei Chang & Kalervo Järvelin, 2022. "Largest contribution to LIS by external disciplines as measured by the characteristics of research articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4499-4522, August.
    9. Manuel A. Vázquez & Jorge Pereira-Delgado & Jesús Cid-Sueiro & Jerónimo Arenas-García, 2022. "Validation of scientific topic models using graph analysis and corpus metadata," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5441-5458, September.

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