People make a range of everyday decisions about how and whether to share content with different people, across different platforms and services, during a variety of tasks. These sharing decisions can encompass complex preferences and a variety of access-control dimensions. In this thesis I examine potential methods for improving sharing mechanisms by better understanding the everyday online sharing environment and evaluating a potential sharing tool.

I first present two studies that explore how current sharing mechanisms may fall short on social networking sites, leading to suboptimal outcomes such as regret or self censorship. I discuss the implications of these suboptimal outcomes for the design of behavioral nudging tools and the potential for improving the design of selective-sharing mechanisms. I then draw on a third study to explore the broader "ecosystem" of available channels created by the services and platforms people move between and combine to share content in everyday contexts. I examine the role of selective-sharing features in the broader audience-driven and task-driven dynamics that drive sharing decisions in this environment. I discuss the implications of channel choice and dynamics for the design of selective-sharing mechanisms.

Using insights from current shortfalls and ecosystem-level dynamics I then present a fourth study examining the potential for adding topic-driven sharing mechanisms to Facebook. I use design mockups and a lab-based interview to explore participants' hypothetical use cases for such mechanisms. I find that these mechanisms could potentially be useful in a variety of situations, but successful implementation would require accounting for privacy requirements and users' sharing strategies.

233 pages

Lorrie Faith Cranor (Chair)
Lujo Bauer
Laura Dabbish (Heinz/HCII)
Moira Burke (Facebook)

William L. Scherlis, Director, Institute for Software Research
Andrew W. Moore, Dean, School of Computer Science

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