Analyzing research trends in personal information privacy using topic modeling

Hyo Shin Choi; Won Sang Lee; So Young Sohn

doi:10.1016/j.cose.2017.03.007

Analyzing research trends in personal information privacy using topic modeling

Hyo Shin Choi, Won Sang Lee, So Young Sohn

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

48 Citations (Scopus)

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.

Original language	English
Pages (from-to)	244-253
Number of pages	10
Journal	Computers and Security
Volume	67
DOIs	https://doi.org/10.1016/j.cose.2017.03.007
Publication status	Published - 2017 Jun 1

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2016R1A2A1A05005270).

Publisher Copyright:
© 2017 Elsevier Ltd

All Science Journal Classification (ASJC) codes

Computer Science(all)
Law

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10.1016/j.cose.2017.03.007

Cite this

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title = "Analyzing research trends in personal information privacy using topic modeling",

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.",

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AU - Lee, Won Sang

AU - Sohn, So Young

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N2 - 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.

AB - 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.

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Analyzing research trends in personal information privacy using topic modeling

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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