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Knowledge structure transition in library and information science: topic modeling and visualization

Author

Listed:
  • Yosuke Miyata

    (Keio University)

  • Emi Ishita

    (Kyushu University)

  • Fang Yang
  • Michimasa Yamamoto
  • Azusa Iwase

    (Keio University)

  • Keiko Kurata

    (Keio University)

Abstract

The purpose of this research is to identify topics in library and information science (LIS) using latent Dirichlet allocation (LDA) and to visualize the knowledge structure of the field as consisting of specific topics and its transition from 2000–2002 to 2015–2017. The full text of 1648 research articles from five peer-reviewed representative LIS journals in these two periods was analyzed by using LDA. A total of 30 topics in each period were labeled based on the frequency of terms and the contents of the articles. These topics were plotted on a two-dimensional map using LDAvis and categorized based on their location and characteristics in the plots. Although research areas in some forms were persistent with which discovered in previous studies, they were crucial to the transition of the knowledge structure in LIS and had the following three features: (1) The Internet became the premise of research in LIS in 2015–2017. (2) Theoretical approach or empirical work can be considered as a factor in the transition of the knowledge structure in some categories. (3) The topic diversity of the five core LIS journals decreased from the 2000–2002 to 2015–2017.

Suggested Citation

  • Yosuke Miyata & Emi Ishita & Fang Yang & Michimasa Yamamoto & Azusa Iwase & Keiko Kurata, 2020. "Knowledge structure transition in library and information science: topic modeling and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 665-687, October.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:1:d:10.1007_s11192-020-03657-5
    DOI: 10.1007/s11192-020-03657-5
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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.
    2. Cassidy R. Sugimoto & Daifeng Li & Terrell G. Russell & S. Craig Finlay & Ying Ding, 2011. "The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 185-204, January.
    3. Kun Lu & Dietmar Wolfram, 2012. "Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(10), pages 1973-1986, October.
    4. Manika Lamba & Margam Madhusudhan, 2019. "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 477-505, August.
    5. 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.
    6. Erjia Yan, 2015. "Research dynamics, impact, and dissemination: A topic-level analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2357-2372, November.
    7. Cassidy R. Sugimoto & Daifeng Li & Terrell G. Russell & S. Craig Finlay & Ying Ding, 2011. "The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 185-204, January.
    8. Yan, Erjia, 2014. "Research dynamics: Measuring the continuity and popularity of research topics," Journal of Informetrics, Elsevier, vol. 8(1), pages 98-110.
    9. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2018. "Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 869-892, May.
    10. Francesca De Battisti & Alfio Ferrara & Silvia Salini, 2015. "A decade of research in statistics: a topic model approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 413-433, May.
    11. Dangzhi Zhao & Andreas Strotmann, 2008. "Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic‐coupling analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(13), pages 2070-2086, November.
    12. Yang, Siluo & Han, Ruizhen & Wolfram, Dietmar & Zhao, Yuehua, 2016. "Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis," Journal of Informetrics, Elsevier, vol. 10(1), pages 132-150.
    13. Jeppe Nicolaisen & Tove Faber Frandsen, 2015. "Bibliometric evolution: Is the journal of the association for information science and technology transforming into a specialty Journal?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(5), pages 1082-1085, May.
    14. 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 American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
    15. Karen E. Pettigrew & Lynne (E.F.) McKechnie, 2001. "The use of theory in information science research," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(1), pages 62-73.
    16. Howard D. White & Belver C. Griffith, 1981. "Author cocitation: A literature measure of intellectual structure," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 32(3), pages 163-171, May.
    17. Kun Lu & Dietmar Wolfram, 2012. "Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(10), pages 1973-1986, October.
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    Cited by:

    1. Wei‐Min Fan & Wei Jeng & Muh‐Chyun Tang, 2023. "Using data citation to define a knowledge domain: A case study of the Add‐Health dataset," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 81-98, January.
    2. 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.
    3. 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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