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

Yosuke Miyata · Emi Ishita · Michimasa Yamamoto · Azusa Iwase · Keiko Kurata

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scientometrics and bibliometrics research Library Science and Information Information Science and Libraries

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 usingLDAvisand 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.

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References

1981Author cocitation: A literature measure of intellectual structure 2018Journal of the Association for Information Science and Technology 2008Studying the history of ideas using topic models 2018Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)2008Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic‐coupling analysis 2000The use of theory in information science research 2011The cognitive structure of Library and Information Science: Analysis of article title words 2012Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches

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