Engagement in Information Search

  • Ashlee Edwards
  • Diane KellyEmail author


This chapter examines user engagement in interactive information search systems, drawing upon research in information retrieval, information behavior, and human-computer interaction. The authors describe small-scale, laboratory-based studies and large-scale, online search studies to illuminate the factors of systems (e.g., architecture), users (e.g. individual differences), and tasks (e.g. degree of complexity) that impact engagement outcomes. Throughout this chapter, the range of self-report, behavioral, and physiological methods that inform information search engagement research, along with their benefits and limitations are discussed. The authors argue that engagement is central to search success but that studying search engagement is challenging due to the number of factors that impact information search. Mixed methods approaches that combine subjective and objective measures can equip researchers to meet these challenges.


Search Task Information Search Search Behavior Search Interface User Engagement 
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.


  1. 1.
    Arapakis, I., Bai, X., Cambazoglu, B.B.: Impact of response latency on user behavior in web search. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 103–112 (2014)Google Scholar
  2. 2.
    Arapakis, I., Lalmas, M., Valkanas, G.: Understanding within-content engagement through pattern analysis of mouse gestures. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1439–1448 (2014)Google Scholar
  3. 3.
    Arapakis, I., Lalmas, M., Cambazoglu, B.B., Marcos, M.C., Jose, J.M.: User engagement in online news: under the scope of sentiment, interest, affect, and gaze. J. Assoc. Inf. Sci. Technol. 65, 1988–2005 (2014)CrossRefGoogle Scholar
  4. 4.
    Arguello, J., Wu, W.C., Kelly, D., Edwards, A.: Task complexity, vertical display and user interaction in aggregated search. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 435–444 (2012)Google Scholar
  5. 5.
    Bateman, S., Teevan, J., White, R.W.: The search dashboard: how reflection and comparison impact search behavior. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1785–1794. ACM Press, New York (2012)Google Scholar
  6. 6.
    Belkin, N.J.: Some(what) grand challenges for information retrieval. SIGIR Forum 42, 47–54 (2008)CrossRefGoogle Scholar
  7. 7.
    Borlund, P.: The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Inf. Res. 8 (3), 1–5 (2003)Google Scholar
  8. 8.
    Borlund, P., Dreier, S., Byström, K.: What does time spent on searching indicate? In: Proceedings of the 4th Information Interaction in Context Symposium, pp. 184–193 (2012)Google Scholar
  9. 9.
    Capra, R., Sams, B., Seligson, P.: Self-generated versus imposed tasks in collaborative search. In: Collaborative Information Seeking: Bridging the Gap Between Theory and Practice (CIS). Workshop at the Meeting of the American Society for Information Science and Technology (2011)Google Scholar
  10. 10.
    Chen, J.V., Lin, C., Yen, D.C., Linn, K.P.: The interaction effects of familiarity, breadth and media usage on web browsing experience. Comput. Hum. Behav. 27, 2141–2152 (2011)CrossRefGoogle Scholar
  11. 11.
    Colbert, M., Boodoo, A.: Does’ letting go of the words’ increase engagement: a traffic study. In: ACM CHI 2011 Extended Abstracts on Human Factors in Computing Systems, pp. 655–667. ACM Press, New York (2011)Google Scholar
  12. 12.
    Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper-Perennial, New York (1991)Google Scholar
  13. 13.
    Duin, A.H., Archee, R.: Distance learning via the world wide web: information, engagement, and community. In: Selber, S. (ed.) Computers and Technical Communication: Pedagogical and Programmatic Perspectives, pp. 149–169. Erlbaum, Hillsdale, NJ (1997)Google Scholar
  14. 14.
    Dupret, G., Lalmas, M.: Absence time and user engagement: evaluating ranking functions. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining, pp. 173–182. ACM Press, New York (2013)Google Scholar
  15. 15.
    Feild, H., White, R.W., Fu, X.: Supporting orientation during search result examination. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2999–3008. ACM Press, New York (2013)Google Scholar
  16. 16.
    Grafsgaard, J.F., Wiggins, J.B., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Automatically recognizing facial expression: predicting engagement and frustration. In: Proceedings of the 6th International Conference on Educational Data Mining, pp. 43–50 (2013)Google Scholar
  17. 17.
    Hearst, M.: Search User Interfaces. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  18. 18.
    Heinström, J.: Broad exploration or precise specificity: two basic information seeking patterns among students. J. Am. Soc. Inf. Sci. Technol. 57, 1440–1450 (2006)CrossRefGoogle Scholar
  19. 19.
    Hwang, M.I., Thorn, R.G.: The effect of user engagement on system success: a meta-analytical integration of research findings. Inf. Manag. 35 (4), 229–336 (1999)CrossRefGoogle Scholar
  20. 20.
    Jacques, R., Precce, J, Carey, J.T.: Engagement as a design concept for hypermedia. Can. J. Educ. Commun. 24, 49–59 (1995)Google Scholar
  21. 21.
    Jansen, B.J.: Search log analysis: what it is, what’s been done, how to do it. Libr. Inf. Sci. Res. 28, 407–432 (2006)CrossRefGoogle Scholar
  22. 22.
    Jiang, J., He, D., Allan, J.: Searching, browsing, and clicking in a search session: changes in user behavior by task and over time. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 607–616. ACM Press, New York (2014)Google Scholar
  23. 23.
    Kelly, D., Sugimoto, C.R.: A systematic review of interactive information retrieval evaluation studies, 1967–2006. J. Am. Soc. Inf. Sci. Technol. 64 (4), 745–770 (2013)CrossRefGoogle Scholar
  24. 24.
    Kelly, D., Arguello, J., Edwards, A., Wu, W.C.: Development and evaluation of search tasks for IIR experiments using a cognitive complexity framework. In: Proceedings of SIGIR International Conference on the Theory of Information Retrieval, pp. 7–10 (2015)Google Scholar
  25. 25.
    Lehmann, J., Lalmas, M., Yom-Tov, E., Dupret, G.: Models of user engagement. In: User Modeling, Adaptation, and Personalization, pp. 164–175 (2012)Google Scholar
  26. 26.
    Lehmann, J., Lalmas, M., Baeza-Yates, R., Yom-Tov, E.: Networked user engagement. In: Proceedings of the 1st Workshop on User Engagement Optimization, pp. 7–10 (2013)Google Scholar
  27. 27.
    Lehmann, J., Lalmas, M., Dupret, G., Baeza-Yates, R.: Online multitasking and user engagement. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 519–528 (2013)Google Scholar
  28. 28.
    Li, Y., Belkin, N.J.: A faceted approach to conceptualizing tasks in information seeking. Inf. Process. Manag. 44, 1822–1837 (2008)CrossRefGoogle Scholar
  29. 29.
    Linnenbrink, E.A., Pintrich, P.R.: The role of self-efficacy beliefs in student engagement and learning in the classroom. Read. Writ. Q. 19 (2), 119–137 (2003)CrossRefGoogle Scholar
  30. 30.
    Moshfeghi, Y., Matthews, M., Blanco, R., Jose, J.M.: Influence of timeline and named-entity components on user engagement. In: Advances in Information Retrieval, pp. 305–317 (2013)Google Scholar
  31. 31.
    Nes, L.S., Segerstrom, S.C., Sephton, S.E.: Engagement and arousal: optimism’s effects during a brief stressor. Personal. Soc. Psychol. Bull. 31 (1), 111–120 (2005)CrossRefGoogle Scholar
  32. 32.
    O’Brien, H., Lebow, M.: Mixed-methods approach to measuring user experience in online news interactions. J. Am. Soc. Inf. Sci. Technol. 64, 1543–1556 (2013)CrossRefGoogle Scholar
  33. 33.
    O’Brien, H., Toms, E.G.: What is user engagement? A conceptual framework for defining user engagement with technology. J. Am. Soc. Inf. Sci. Technol. 59, 938–955 (2008)Google Scholar
  34. 34.
    O’Brien, H., Toms, E.G.: The development and evaluation of a survey to measure user engagement. J. Am. Soc. Inf. Sci. Technol. 61, 50–69 (2010)CrossRefGoogle Scholar
  35. 35.
    O’Brien, H., Toms, E.G.: Examining the generalizability of the user engagement scale (UES) in exploratory search. Inf. Process. Manag. 49, 1092–1107 (2013)CrossRefGoogle Scholar
  36. 36.
    Ortiz-Cordova, A., Jansen, B.J.: Classifying web search queries to identify high revenue generating customers. J. Am. Soc. Inf. Sci. Technol. 63, 1426–1441 (2012)CrossRefGoogle Scholar
  37. 37.
    Poddar, A., Ruthven, I.: The emotional impact of search tasks. In: Proceedings of the 3rd Symposium on Information Interaction in Context, pp. 35–44 (2010)Google Scholar
  38. 38.
    Reeves, B., Nass, C.: The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press, New York, NY (1996)Google Scholar
  39. 39.
    Rokhlenko, O., Golbandi, N., Lempel, R., Leibovich, L.: Engagement-based user attention distribution on web article pages. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media, pp. 196–201 (2013)Google Scholar
  40. 40.
    Schaufeli, W.B., Salanova, M.: Enhancing work engagement through the management of human resources. In: Naswall, K., Hellgren, J., Sverke, M. (eds.) The Individual in the Changing Working Life, pp. 380–402. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  41. 41.
    Song, Y., Shi, X., Fu, X.: Evaluating and predicting user engagement change with degraded search relevance. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1213–1224 (2013)Google Scholar
  42. 42.
    Sundar, S.S., Xu, Q., Bellur, S., Oh, J., Jia, H.: Beyond pointing and clicking: how do newer interaction modalities affect user engagement? In: CHI’11 Extended Abstracts on Human Factors in Computing Systems. ACM Press, New York (2011)Google Scholar
  43. 43.
    Teevan, J., Collins-Thompson, K., White, R.W., Dumais, S.T., Kim, Y.: Slow search: information retrieval without time constraints. In: Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval, pp. 1–10 (2013)Google Scholar
  44. 44.
    Toms, E.: Task-based information searching and retrieval. In: Ruthven, I., Kelly, D. (eds.) Task-Based Information Searching and Retrieval, pp. 43–59 (2011)Google Scholar
  45. 45.
    Vail, A.K., Grafsgaard, J.F., Wiggins, J.B., Lester, J.C., Boyer, K.E.: Predicting learning and engagement in tutorial dialogue: a personality-based model. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 255–262 (2014)Google Scholar
  46. 46.
    Webster, J., Ahuja, J.S.: Enhancing the design of web navigation systems: the influence of user disorientation on engagement and performance. MIS Q. 30, 661–678 (2006)Google Scholar
  47. 47.
    Wildemuth, B.W., Freund, L., Toms, E.G.: Untangling search task complexity and difficulty in the context of interactive information retrieval studies. J. Doc. 70, 1118–1140 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of North Carolina at Chapel HillChapel HillUSA

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