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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)

Abstract

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.

Keywords

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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References

  1. 1.
    González, V.M., Mark, G.: Constant, constant, multi-tasking craziness: Managing multiple working spheres. In: Dykstra-Erickson, E., Tscheligi, M. (eds.) CHI, pp. 113–120. ACM, New York (2004)Google Scholar
  2. 2.
    Rouncefield, M., Hughes, J.A., Rodden, T., Viller, S.: Working with “constant interruption”: CSCW and the small office. In: CSCW, pp. 275–286 (1994)Google Scholar
  3. 3.
    Su, N.M., Mark, G.: Designing for nomadic work. In: van der Schijff, J., Marsden, G. (eds.) Conference on Designing Interactive Systems, pp. 305–314. ACM, New York (2008)CrossRefGoogle Scholar
  4. 4.
    Patterson, D.J., Ding, X., Noack, N.: Nomatic: Location by, for, and of crowds. In: Hazas, M., Krumm, J., Strang, T. (eds.) LoCA 2006. LNCS, vol. 3987, pp. 186–203. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Hudson, J.M., Christensen, J., Kellogg, W.A., Erickson, T.: “I’d be overwhelmed, but it’s just one more thing to do”: Availability and interruption in research management. In: CHI ’02: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 97–104. ACM, New York (2002)Google Scholar
  6. 6.
    Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper Perennial (March 1991)Google Scholar
  7. 7.
    Horvitz, E., Koch, P., Apacible, J.: Busybody: creating and fielding personalized models of the cost of interruption. In: Herbsleb, J.D., Olson, G.M. (eds.) CSCW, pp. 507–510. ACM, New York (2004)CrossRefGoogle Scholar
  8. 8.
    Fogarty, J., Hudson, S.E., Atkeson, C.G., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J.C., Yang, J.: Predicting human interruptibility with sensors. ACM Trans. Comput.-Hum. Interact. 12(1), 119–146 (2005)CrossRefGoogle Scholar
  9. 9.
    Iqbal, S.T., Bailey, B.P.: Understanding and developing models for detecting and differentiating breakpoints during interactive tasks. In: Rosson, M.B., Gilmore, D.J. (eds.) CHI, pp. 697–706. ACM, New York (2007)Google Scholar
  10. 10.
    Shen, J., Irvine, J., Bao, X., Goodman, M., Kolibaba, S., Tran, A., Carl, F., Kirschner, B., Stumpf, S., Dietterich, T.G.: Detecting and correcting user activity switches: algorithms and interfaces. In: Conati, C., Bauer, M., Oliver, N., Weld, D.S. (eds.) IUI, pp. 117–126. ACM, New York (2009)Google Scholar
  11. 11.
    Ho, J., Intille, S.S.: Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. In: van der Veer, G.C., Gale, C. (eds.) CHI, pp. 909–918. ACM, New York (2005)Google Scholar
  12. 12.
    Kapoor, A., Horvitz, E.: Experience sampling for building predictive user models: a comparative study. In: CHI, April 2008, pp. 657–666 (2008)Google Scholar
  13. 13.
    Ding, X., Patterson, D.J.: Status on display: a field trial of Nomatic*Viz. In: Wagner, I., Tellioğlu, H., Balka, E., Simone, C., Ciolfi, L. (eds.) ECSCW 2009. Computer Science, pp. 303–322. Springer, London (2009)CrossRefGoogle Scholar
  14. 14.
    Smale, S., Greenberg, S.: Broadcasting information via display names in instant messaging. In: GROUP ’05: Proc. of the 2005 Intl ACM SIGGROUP Conference on Supporting group work, pp. 89–98. ACM, New York (2005)CrossRefGoogle Scholar
  15. 15.
    Cheverst, K., Dix, A., Fitton, D., Rouncefield, M., Graham, C.: Exploring awareness related messaging through two situated-display-based systems. Hum.-Comput. Interact. 22(1), 173–220 (2007)CrossRefGoogle Scholar
  16. 16.
    Dourish, P.: What we talk about when we talk about context. Personal and Ubiquitous Computing 8(1), 19–30 (2004)CrossRefGoogle Scholar
  17. 17.
    Patterson, D.J., Ding, X., Kaufman, S.J., Liu, K., Zaldivar, A.: An ecosystem for learning and using sensor-driven IM messages. IEEE Pervasive Computing 8(4), 42–49 (2009)CrossRefGoogle Scholar

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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