Research trends in social network analysis using topic modeling and network analysis

This study investigates the research trends in social network analysis (SNA) by examining related scholarly articles. Firstly, topic modeling is applied to a corpus composed of the title, abstract, keywords, and author information of 58,122 articles obtained from the Scopus database. Twenty topics and their ascending or descending trends are identified. Secondly, to explore the multidisciplinary nature of SNA, an academic field network is constructed based on co-authorship and affiliation information. Results show that physics, biology, and education have taken central position in the network. It was also interesting to see that sociology and psychology are less influential. The results of this study will be helpful for future researchers exploring SNA research topics and thereby improve the diversity of SNA research.