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

  • Justin Tang
  • Donald J. Patterson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6075)


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.


User Interface Sensor Reading Instant Messaging Status Update Status Message 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Justin Tang
    • 1
  • Donald J. Patterson
    • 1
  1. 1.University Of CaliforniaIrvineUSA

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