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Research on the Follow-up Actions of College Students' Mobile Search

Published:19 June 2016Publication History

ABSTRACT

This paper focuses on the follow-up actions triggered by college students' mobile searches, which involved 30 participants conducting an uncontrolled experiment in fifteen days. We collected the mobile phone usage data by an app called AWARE, and combined with structured diary and interviews to perform a quantitative and qualitative study. The results showed that, there were three categories of follow-up actions and majority of these actions occurred within one hour after the initial search session. We also found that participants often conducted follow-up actions with different apps, and certain information needs triggered more follow-up actions. We finally discussed the characteristics and the causes of these actions, and stated further studies which include comparing follow-up actions triggered by mobile search and that of Web search, and building a model for the follow-up actions.

References

  1. Arter, D., Buchanan, G., Jones, M. and Harper, R. 2007. Incidental information and mobile search. In Proceedings of the International Conference on Human Computer Interaction with Mobile Devices & Services (Singapore, September 9--12, 2007). MobileHCI'07, ACM, New York, NY, 413--420. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Chen, X., Sin, S. C. J., Theng, Y. L. and Lee, C. S. 2015. Why do social media users share misinformation?. In Proceedings of the Joint Conference on Digital Libraries (Knoxville, Tennessee, USA, June 21--25, 2015). JCDL'15, ACM, New York, NY, 111--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Kamvar, M. and Baluja, S. 2007. Deciphering trends in mobile search. Computer, 40(8), 58--62. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Daoud, M., Lechani, L. T. and Boughanem, M. 2009. Towards a graph-based user profile modeling for a session-based personalized search. Knowledge & Information Systems, 21(3), 365--398.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Eickhoff, C., Teevan, J., White, R. and Dumais, S. 2014. Lessons from the journey: a query log analysis of within-session learning. In Proceedings of International Conference on Web Search and Data Mining (New York, NY, USA, February 24--28,2014). WSDM'14, ACM, New York, NY, 223--232. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Fu, H. and Wu, S. 2015. Studying Chinese-English mixed language queries from the user perspectives. In Proceedings of the Joint Conference on Digital Libraries (Knoxville, Tennessee, USA, June 21--25, 2015). JCDL'15, ACM, New York, NY, 247--248. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Kamvar, M., Kellar, M., Patel, R. and Xu, Y. 2009. Computers and iPhones and mobile phones, my!: a logs-based comparison of search users on different devices. In Proceedings of International Conference on World Wide Web (Madrid, Spain, April 20--24, 2009). WWW'09, 801--810. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Kotov, A., Bennett, P. N., White, R. W., Dumais, S. T. and Teevan, J. 2011. Modeling and analysis of cross-session search tasks. In Proceedings of SIGIR Conference on Research and Development in Information Retrieval (Beijing, China, July 24--28, 2011). SIGIR'11, ACM, New York, NY, 5--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Montanez, G. D., White, R. W. and Huang, X. 2014. Cross-device search. In Proceedings of the International Conference on Information and Knowledge Management (Shanghai, China, November 3--7, 2014). CIKM'14, ACM, New York, NY, 1669--1678. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Sohn, T., Mori, K. and Setlur, V. 2010. Enabling cross-device interaction with web history. In Proceedings of International Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA, April 10--15, 2010). CHI '10, ACM, New York, NY, 3883--3888. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Ghose, A., Goldfarb, A., & Han, S. P. (2012). How is the mobile Internet different? Search costs and local activities. Information Systems Research, 24(3), 613--631.Google ScholarGoogle ScholarCross RefCross Ref
  12. Teevan, J., Karlson, A., Amini, S., Brush, A. J. B. and Krumm, J. 2011. Understanding the importance of location, time, and people in mobile local search behavior. In Proceedings of International Conference on Human Computer Interaction with Mobile Devices & Services (Stockholm, Sweden, August 30- September 2 ,2011). MobileHCI'11, ACM, New York, NY ,77--80. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Conferences
      JCDL '16: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries
      June 2016
      316 pages
      ISBN:9781450342292
      DOI:10.1145/2910896

      Copyright © 2016 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 19 June 2016

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      JCDL '16 Paper Acceptance Rate15of52submissions,29%Overall Acceptance Rate415of1,482submissions,28%

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