This paper presents CoCo, a system that automates web tasks on a user's behalf through an interactive conversational interface. Given a short command such as "get road conditions for highway 88," CoCo synthesizes a plan to accomplish the task, executes it on the web, extracts an informative response, and returns the result to the user as a snippet of text. A novel aspect of our approach is that we leverage a repository of previously recorded web scripts and the user's personal web browsing history to determine how to complete each requested task. This paper describes the design and implementation of our system, along with the results of a brief user study that evaluates how likely users are to understand what CoCo does for them.

Recent research has developed analytics that threaten online self-presentation and privacy by automatically generating profiles of individuals' most personal traits-their personality, values, motivations, and so on. But we know little about people's reactions to personal traits profiles of themselves, or what influences their decisions to share such profiles. We present an early qualitative study of people's reactions to a working hyper-personal analytics system. The system lets them see their personality and values profile derived from their own social media text. Our results reveal a paradox. Participants found their personal traits profiles creepily accurate and did not like sharing them in many situations. However, they felt pressured by the social risks of not sharing and showed signs of learned helplessness, leading them to share despite their misgivings. Further, they felt unqualified to significantly modify their profile contents due to a surprising trust in the "expert" algorithm. We explore design implications for hyper-personal analytics systems that consider the needs and preferences of the people being profiled, suggesting ways to enhance the control they feel and the benefits they reap.

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