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.
Highlights
BackgroundInvestigating the kind of research being done in a field of research involves understanding the knowledge structure of that field and, in turn, revealing the identity of that field
For the categorization of topics into research areas (RQ1), we found some commonalities with the results of previous studies using co-citation analysis and content analysis
We explored the transition of knowledge structure of library and information science (LIS) in the years 2000–2002 and 2015–2017, using latent Dirichlet allocation (LDA)
Summary
Investigating the kind of research being done in a field of research involves understanding the knowledge structure of that field and, in turn, revealing the identity of that field. Scientometrics (2020) 125:665–687 library and information science (LIS), such investigations have been undertaken since the 1970s using a variety of approaches. The topic modeling approach has recently garnered considerable attention. This approach is a type of big data analysis of words in articles that can reveal hidden relationships between them and can sometimes find nonthematic topics. This article uses this topic modeling approach to clarify the knowledge structure of LIS
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