Analyzing research trends in personal information privacy using topic modeling

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Date: June 2017
From: Computers & Security(Vol. 67)
Publisher: Elsevier B.V.
Document Type: Article
Length: 150 words

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Abstract :

To link to full-text access for this article, visit this link: Byline: Hyo Shin Choi, Won Sang Lee, So Young Sohn 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. Article History: Received 2 March 2016; Revised 6 February 2017; Accepted 12 March 2017

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Gale Document Number: GALE|A492010291