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

• Published in: Scientometrics Vol. 125; no. 1; pp. 665 - 687
• Main Authors: Miyata, Yosuke, Ishita, Emi, Yang, Fang, Yamamoto, Michimasa, Iwase, Azusa, Kurata, Keiko
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-10-2020; Springer Nature B.V
• Subjects: Computer Science; Dirichlet problem; Empirical analysis; Information Storage and Retrieval; Libraries; Library and information science; Library Science; Visualization; Topic modeling; Research trend; Visualization; Latent Dirichlet allocation; Library and information science
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-020-03657-5

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.