Content Analysis of Privacy Management Features in Geosocial Networking Application

Abstract

Geosocial networking application allows user to share information and communicate with other people within a virtual neighborhood or community. Although most geosocial networking application include privacy management features, one the challenge is to improve privacy management features design. To overcome this challenge, the adaptation of privacyrelated theories offers a concrete way to comprehend and analyze how the privacy management features are used as tangible research results that facilitate user and system developer in understanding privacy management. This paper attempt to propose a standardized privacy management features in geosocial networking application from market perspectives that could be utilized by researchers and application developers to demonstrate or measure privacy management features. The objective of this paper is twofold: First, to map the theoretical constructs guided by Communication Privacy Management (CPM) theory into privacy management features in geosocial networking application. Second, to evaluate the reliability of the proposed features using content analysis. Content analysis is performed on 1326 geosocial networking apps in the market (Google Play store and App Store) to determine the reliability of the proposed privacy management features through inter-coder reliability analysis. The primary findings of the content analysis show that many of the privacy management features with low reliability are from Boundary Turbulence construct. Furthermore, only 6 out of 13 proposed features are deemed reliable, namely, specific grouping, visibility setting, privacy policy, violation, imprecision and inaccuracy. The proposed privacy management features may aid researchers and system developers to focus on the best privacy management features for improving geosocial networking application design.

User Privacy Protection Behavior and Information Sharing in Mobile Health Application

“…The observation could be done through continuous data collection over a long period [11] using daily location tracking, daily medicine consuming reminders, and others. These require appropriate data management [12]. Inappropriate data management will make people reluctant to share their personal information, as they are aware of the likelihood of their data being misused or exploited.…”