Extended Abstract (pdf)
Privacy research has played an important role in understanding usage concerns and adoption barriers for diverse online technologies . Privacy research on e-commerce, organizational IT, and website use has led to the development of many scales and frameworks for evaluating and comparing concerns about data protection and informational privacy [3, 7, 10, 16]. See sidebar for scales used to measure this type of privacy.
Now that social media is a leading daily internet activi- ty, privacy researchers must also be concerned with interpersonal privacy concerns. Many studies have uncovered social privacy concerns such as accessibility , self-presentation , and self-realization . These findings suggest that social privacy concerns are not well captured by previous scales, which may explain why the scales have been of limited use for measuring privacy in social technologies (e.g. ).
A diversity of perspectives from allied disciplines including social psychology, law, and economics can inform our understanding of privacy concerns asso- ciated with social technology use [1, 2, 8, 10, 11, 16]. Recently these frameworks have been applied to specific social media technologies and contexts [6, 9, 13, 14, 17].
The diversity of privacy frameworks results in various ways to conceptualize and empirically evaluate privacy in social media. As a result, it is difficult to compare results across studies. Even studies drawing from the same framework use study-specific measures that do not easily lend themselves to cross-study comparisons. Thus, the framework can be difficult to adapt to other studies. Developing systematic metrics, such as the scales used for data protection privacy, would allow comparing privacy concerns across populations and samples, as well as across different technologies.
We invite the research community to join this workshop to address a number of key challenges in achieving this research vision: How can general privacy measures be useful across different studies? How can we measure whether one design is more effective than another in addressing end-user privacy concerns? What is the role of various qualitative and quantitative methods in developing metrics? How can we draw from existing social privacy frameworks to contribute to our understanding of social privacy in networked situations?
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