Twitter, Sensors and UI: Robust Context Modeling for Interruption Management

In this paper, we present the results of a two-month field study of fifteen people using a software tool designed to model changes in a user’s availability. The software uses status update messages, as well as sensors, to detect changes in context. When changes are identified using the Kullback-Leibler Divergence metric, users are prompted to broadcast their current context to their social networks. The user interface method by which the alert is delivered is evaluated in order to minimize the impact on the user’s workflow. By carefully coupling both algorithms and user interfaces, interruptions made by the software tool can be made valuable to the user.

• Type: Conference
• Year: 2010
• Where: UM
• Authors: Justin Tang, Donald J. Patterson
• Keywords: Software Tool, Two-month field Study, UM 2010, User Interfaces