Exploring the Utility Versus Intrusiveness of Dynamic Audience Selection on Facebook

Abstract

In contrast to existing, static audience controls that map poorly onto users' ideal audiences on social networking sites, dynamic audience selection (DAS) controls can make intelligent inferences to help users select their ideal audience given context and content. But does this potential utility outweigh its potential intrusiveness? We surveyed 250 participants to model users' ideal versus their chosen audiences with static controls and found a significant misalignment, suggesting that DAS might provide utility. We then designed a sensitizing prototype that allowed users to select audiences based on personal attributes, content, or context constraints. We evaluated DAS vis-a-vis Facebook's existing audience selection controls through a counterbalanced summative evaluation. We found that DAS's expressiveness, customizability, and scalability made participants feel more confident about the content they shared on Facebook. However, low transparency, distrust in algorithmic inferences, and the emergence of privacy-violating side channels made participants find the prototype unreliable or intrusive. We discuss factors that affected this trade-off between DAS's utility and intrusiveness and synthesize design implications for future audience selection tools.