ACM Human Factors in Computing Systems (CHI), 2016
Participants in social media systems must balance many considerations when choosing what to share and with whom. Sharing with others invites certain risks, as well as potential benefits; achieving the right balance is even more critical when sharing photos, which can be particularly engaging, but potentially compromising. In this paper, we examine photo-sharing decisions as an interaction between high-level user preferences and specific features of the images being shared. Our analysis combines insights from a 96-user survey with metadata from 10.4M photos to develop a model integrating these perspectives to predict permissions settings for uploaded photos. We discuss implications, including how such a model can be applied to provide online sharing experiences that are more safe, more scalable, and more satisfying.