Analyzing research trends in personal information privacy using topic modeling. Issue 67 (June 2017)

• Record Type: Journal Article
• Authors: Choi, Hyo Shin; Lee, Won Sang; Sohn, So Young
• Is Part Of: Computers & security. Issue 67(2017)
• Page Start: 244
• Page End: 253
• Publication Date: 2017-06
• Subjects: Privacy -- Personal information -- Text mining -- Topic model -- Latent Dirichlet allocation -- Literature survey; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2017.03.007
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: This study examines trends in academic research on personal information privacy. Using Scopus DB, we extracted 2356 documents covering journal articles, reviews, book chapters, conference papers, and working papers published between 1972 and August 2015. Latent Dirichlet allocation (LDA) is applied to the abstracts of those extracted documents to identify topics. Topics discovered from all documents focus mainly on technology, and the findings indicate that algorithms, Facebook privacy, and online social networks have become prominent topics. In contrast, it was observed that journal articles put more emphasis on both the e-business and healthcare. These results identify a research gap in the area of personal information privacy and offer a direction for future research.