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

Yosuke Miyata (), Emi Ishita, Fang Yang, Michimasa Yamamoto, Azusa Iwase and Keiko Kurata
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Yosuke Miyata: Keio University
Emi Ishita: Kyushu University
Azusa Iwase: Keio University
Keiko Kurata: Keio University

Scientometrics, 2020, vol. 125, issue 1, No 28, 665-687

Abstract: 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.

Keywords: Library and information science; Latent Dirichlet allocation; Topic modeling; Visualization; Research trend (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:125:y:2020:i:1:d:10.1007_s11192-020-03657-5

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DOI: 10.1007/s11192-020-03657-5

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