skip to main content
10.1145/1357054.1357062acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Knowledge in the head and on the web: using topic expertise to aid search

Published:06 April 2008Publication History

ABSTRACT

The importance of background knowledge for effective searching on the Web is not well understood. Participants were given trivia questions on two topics and asked to answer them first using background knowledge and second by searching on the Web. Knowledge of a topic predicted search performance on that topic for all questions and, more importantly, for questions for which participants did not already know the answer. In terms of process, greater topic knowledge led to less time being spent on each Webpage, faster decisions to give up a line of inquiry and shorter queries being entered into the search engine. A more complete theory-led understanding of these effects would assist workers in a whole range of Web-related professions.

References

  1. Bhavnani, S.K. (2001). Important Cognitive Components of Domain-Specific Search Knowledge. Proceedings of TREC'2001, 571--578.Google ScholarGoogle Scholar
  2. Cohen, J. (1988). Statistical power for the behavioral sciences (2nd Edition). Hillsdale, NJ: Erlbaum.Google ScholarGoogle Scholar
  3. Fu, W.-T., Pirolli, P. (in press), SNIF-ACT: A Model of Information-Seeking Behavior in the World Wide Web. Human-Computer Interaction.Google ScholarGoogle Scholar
  4. Greenfield, S. (2003). How tomorrow's technology is changing the way we think and feel. London, Penguin.Google ScholarGoogle Scholar
  5. Hembrooke, H.A., Gay, G.K., Granka, L.A. (2005), "The effects of expertise and feedback on search term selection and subsequent learning", Journal of the American Society for Information Science and Technology, 56(8), 861--71. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Hölscher, C., & G. Strube. (2000). Web search behavior of internet experts and newbies. Computer Networks, 33(1), 337--346. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Hsieh-Yee, I. (1993). Effects of search experience and subject knowledge on the search tactics of novice and experienced searchers. Journal of the American Society for Information Science and Technology, 44(3), 161--174.Google ScholarGoogle ScholarCross RefCross Ref
  8. James, C. Helplessly advanced. On clever machines. http://www.clivejames.com/point-of-view/helplessly-advanced.Google ScholarGoogle Scholar
  9. Jansen, B.J., & Pooch, U. (2000). A review of web searching studies and a framework for future research. Journal of the American Society of Information Science and Technology, 52(3), 235--246. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Jansen, B.J., & Spink, A. (2003). An analysis of Web documents retrieved and viewed. Proceedings of the 4th International Conference on Internet Computing. 65--69, Las Vegas, NV.Google ScholarGoogle Scholar
  11. Kintsch, W. (1998). Comprehension A paradigm for cognition. New York: Cambridge University Press.Google ScholarGoogle Scholar
  12. Kuhlthau, Carol C. (1991). Inside the Search Process: Information Seeking from the User's Perspective. Journal of the American Society of Information Science, 42, 361--371.Google ScholarGoogle ScholarCross RefCross Ref
  13. Lazonder, A., Bieiemans, H., & Wopereis, I. (2002). Differences between novice and experienced users in searching for information on the World Wide Web. Journal of the American Society for Information Science, 51, 576--581. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. MacCallum, R.C., Zhang, S., Preacher, K.J., & Rucker, D.D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19--40.Google ScholarGoogle ScholarCross RefCross Ref
  15. Marchionini, G. (1997). Information seeking in electronic environments. Cambridge, Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Mat-Hassan, M., & Levene, M. (2005). Associating search and navigation behavior through log analysis. Journal of the American Society for Information Science and Technology, 56(9), 913--934. Google ScholarGoogle ScholarCross RefCross Ref
  17. Payne, S. J., Duggan, G. B., & Neth, H. (2007). Discretionary task interleaving: Heuristics for time allocation in cognitive foraging. Journal of Experimental Psychology: General, 136(3), 370--388.Google ScholarGoogle ScholarCross RefCross Ref
  18. Sellen, A.J., Murphy, R., & Shaw, K.L. (2002). How knowledge workers use the Web. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 227--234, Minneapolis, MN. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Tabachnick, B. G., & Fidell, L. S. (1989). Using multivariate statistics (2nd Edition). Cambridge, MA: Harper & Row. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Wildemuth, B. M. (2003). The effects of domain knowledge on search tactic formulation. Journal of the American Society for Information Science, 55, 246--258. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Knowledge in the head and on the web: using topic expertise to aid search

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
        April 2008
        1870 pages
        ISBN:9781605580111
        DOI:10.1145/1357054

        Copyright © 2008 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 6 April 2008

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        CHI '08 Paper Acceptance Rate157of714submissions,22%Overall Acceptance Rate6,199of26,314submissions,24%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader