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Investigating User Perceptions of Mobile App Privacy: An Analysis of User-Submitted App Reviews

Investigating User Perceptions of Mobile App Privacy: An Analysis of User-Submitted App Reviews

Andrew R. Besmer, Jason Watson, M. Shane Banks
Copyright: © 2020 |Volume: 14 |Issue: 4 |Pages: 18
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781799805380|DOI: 10.4018/IJISP.2020100105
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MLA

Besmer, Andrew R., et al. "Investigating User Perceptions of Mobile App Privacy: An Analysis of User-Submitted App Reviews." IJISP vol.14, no.4 2020: pp.74-91. http://doi.org/10.4018/IJISP.2020100105

APA

Besmer, A. R., Watson, J., & Banks, M. S. (2020). Investigating User Perceptions of Mobile App Privacy: An Analysis of User-Submitted App Reviews. International Journal of Information Security and Privacy (IJISP), 14(4), 74-91. http://doi.org/10.4018/IJISP.2020100105

Chicago

Besmer, Andrew R., Jason Watson, and M. Shane Banks. "Investigating User Perceptions of Mobile App Privacy: An Analysis of User-Submitted App Reviews," International Journal of Information Security and Privacy (IJISP) 14, no.4: 74-91. http://doi.org/10.4018/IJISP.2020100105

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Abstract

Mobile devices and third-party applications are used by over 4.5 billion people worldwide. Third-party applications often request or even require authorized access to personal information through mobile device components. Application developers explain the need for access in their privacy policies, yet many users are concerned about the privacy implications of allowing access to their personal information. This article explores how user perceptions of privacy affect user sentiment by analyzing over five million user-submitted text reviews and star ratings collected over a four-year period. The authors use supervised machine learning to classify privacy and non-privacy-related reviews. The authors then use natural language processing sentiment analysis to compare differences between the groups. Additionally, the article explores various aspects of both privacy and non-privacy-related reviews using self-reported measurements such as star rating and helpfulness tags.

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