Today, technology is enabling people to share information on an unprecedented scale. Although much of this information is intended to be shared with a large group of people or even the public, some disclosure is intended for smaller audiences—a subset of a larger group. People may want to limit information visibility because the information is private or sensitive, or they may feel others would not be interested in the content. When people want to selectively share to different audiences, many technologies fail to provide usable mechanisms to manage these more complex sharing situations. In many cases, people lack understanding about which audiences are able to see what items of information. Additionally, the effort to manage audiences and control access to information adds some extra physical and cognitive burden. This research suggests two methods to help people better understand and control sharing. The first examines audience-centric design: using mechanisms that integrate with the primary task and allow sharing to multiple audiences to improve understanding of how information flows to multiple groups of people. The second method examines using community feedback to enhance privacy/sharing default settings thereby lessening the user’s configuration burden. This knowledge contributes to existing research by understanding the extent of how users share information to multiple audiences and react to community feedback mechanisms designed to ease configuration burden
