research-article

How does search behavior change as search becomes more difficult?

Authors:

Zhiwei GuanAuthors Info & Claims

CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 35 - 44

https://doi.org/10.1145/1753326.1753333

Published: 10 April 2010 Publication History

Abstract

Search engines make it easy to check facts online, but finding some specific kinds of information sometimes proves to be difficult. We studied the behavioral signals that suggest that a user is having trouble in a search task. First, we ran a lab study with 23 users to gain a preliminary understanding on how users' behavior changes when they struggle finding the information they're looking for. The observations were then tested with 179 participants who all completed an average of 22.3 tasks from a pool of 100 tasks. The large-scale study provided quantitative support for our qualitative observations from the lab study. When having difficulty in finding information, users start to formulate more diverse queries, they use advanced operators more, and they spend a longer time on the search result page as compared to the successful tasks. The results complement the existing body of research focusing on successful search strategies.

References

[1]

Aula, A. (2003) Query Formulation in Web Information Search. Proc. IADIS WWW/Internet 2003, 403--410.

[2]

Aula, A., Majaranta, P., and Räihä, K.-J. (2005) Eye-tracking reveals the personal styles for search result evaluation. Proceedings of Human-Computer Interaction -- INTERACT 2005, 1058--1061.

Digital Library

[3]

Aula, A. & Nordhausen, K. (2006) Modeling successful performance in web searching. Journal of the American Society for Information Science and Technology, 57(12), 1678--1693.

Digital Library

[4]

Aula, A. and Siirtola, H. (2005) Hundreds of folders or one ugly pile -- strategies for information search and re-access. Proc. INTERACT 2005, 954--957.

Digital Library

[5]

Brand-Gruwel, S., Wopereis, I. and Vermetten, Y. (2005) Information problem solving by experts and novices: analysis of a complex cognitive skill. Computers in Human Behavior, 21, 487--508.

Digital Library

[6]

Downey, D., Dumais, S., Liebling, D., and Horvitz, E. (2008) Understanding the relationship between searchers' queries and information goals. CIKM'08, 449--458.

Digital Library

[7]

Eastman, C.M. and Jansen, B.J. (2003) Coverage, relevance, and ranking: the impact of query operators on web search engine results. ACM Transactions on Information Systems, 21(4), 383--411.

Digital Library

[8]

Fields, B., Keith, S. and Blandford, A. (2004) Designing for expert information finding strategies. Proc. of HCI 2004, 89--102.

[9]

Granka, L.A., Joachims, T. Gay, G. (2004) Eye-tracking analysis of user behavior in WWW search. Proc. SIGIR'04, 478--479.

Digital Library

[10]

Huntington, P., Nicholas, D., and Jamali, H.R. (2007) Employing log metrics to evaluate search behavior and success: case study BBC search engine. Journal of Information Science, 33(5), 584--597.

Digital Library

[11]

Hölscher, C. and Strube, G. (2000) Web search behavior of internet experts and newbies. Proc. WWW 2000, 337--346.

Digital Library

[12]

Jansen, B. and Spink, A. (2006) How are we searching the world wide web? A comparison of nine search engine transaction logs. Information Processing and Management, 42, 248--263.

Digital Library

[13]

Jansen, B., Spink, A. and Saracevic, T. (1998) Real life information retrieval: a study of user queries on the web. SIGIR Forum, 32(1), 5--17.

Digital Library

[14]

Jansen, B., Spink, A. and Saracevic, T. (2000) Real life, real users, and real needs: A study and analysis of user queries on the web. Information Processing and Management, 36(2), 207--227.

Digital Library

[15]

Jenkins, C., Corritore, C.L. and Wiedenbeck, S. (2003) Patterns of information seeking on the web: a qualitative study of domain expertise and web expertise. IT & Society, 1(3), 64--89.

[16]

Kamvar, M., Kellar, M., Patel, R. and Xu, Y. (2009) Computers and iPhones and mobile phones, oh my! A logs-based comparison of search users on different devices. In Proc. WWW 2009, 801--810.

Digital Library

[17]

Khan, K. and Locatis, C. (1998) Searching through the cyberspace: the effects of link display and link density on information retrieval from hypertext on the World Wide Web. Journal of the American Society for Information Science, 49(2), 176--182.

Digital Library

[18]

Lazonder, A.W., Biemans, H.J.A. and Worpeis, I.G.J.H. (2000) Differences between novice and experienced users in searching information on the World Wide Web. Journal of American Society for Information Science, 51(6), 576--581.

Digital Library

[19]

Navarro-Prieto, R., Scaife, M. and Rogers, Y. (1999) Cognitive strategies in web searching. Proc. 5th Conference on Human Factors and the Web. Retrieved September 15, 2009, http://zing.ncsl.nist.gov/hfweb/proceedings/navarro-prieto/

[20]

Rose, D.E. and Levinson, D. (2004) Understanding user goals in Web search. In Proc. WWW 2004, 13--19.

Digital Library

[21]

Silverstein, C., Henzinger, M., Marais, H. and Moricz, M. (1999) Analysis of a very large web search engine query log. SIGIR Forum, 33(1), 6--12.

Digital Library

[22]

Saito, H. and Miwa, K. (2002) A cognitive study of information seeking processes in the WWW: the effects of searcher's knowledge and experience. Proc. WISE'01, 321--333.

Digital Library

[23]

Spink, A., Wolfram, D., Jansen, B.J. and Saracevic, T. (2002) From e-sex to e-commerce: web search changes. IEEE Computer, 35(3), 107--109.

Digital Library

[24]

Teevan, J. Alvarado, C., Ackerman, M.S. and Karger, D.R. (2004) The perfect search engine is not enough: a study of orienteering behavior in directed search. Proc. CHI'2004, 415--422.

Digital Library

[25]

White, R.W. and Drucker, S.M. (2007) Investigating behavioral variability in web search. Proc. WWW 2007, 21--30.

Digital Library

[26]

White, R.W. and Morris, D. (2007) Investigating the querying and browsing behavior of advanced search engine users. Proc. SIGIR 2007, 255--262.

Digital Library

Cited By

Vuong TJacucci GRuotsalo T(2024)Naturalistic Digital Behavior Predicts Cognitive AbilitiesACM Transactions on Computer-Human Interaction10.1145/366034131:3(1-32)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3660341
Kong HYadav SLowy RRuzinov DKim J(2024)Understanding Online Job and Housing Search Practices of Neurodiverse Young Adults to Support Their IndependenceProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642578(1-14)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642578
Smirnova A(2024)Syntactic Variation in Reduced Registers Through the Lens of the Parallel ArchitectureTopics in Cognitive Science10.1111/tops.12747Online publication date: 4-Jul-2024
https://doi.org/10.1111/tops.12747
Show More Cited By

Index Terms

How does search behavior change as search becomes more difficult?
1. Human-centered computing

Recommendations

Search engine user behaviour: How can users be guided to quality content?
ICSTI 2007 and 2008

The typical behaviour of the Web search engine user is widely known: a user only types in one or a few keywords and expects the search engine to produce relevant results in an instant. Search engines not only adapt to this behaviour. On the contrary, ...
Domain expert topic familiarity and search behavior
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Users of information retrieval systems employ a variety of strategies when searching for information. One factor that can directly influence how searchers go about their information finding task is the level of familiarity with a search topic. We ...
An exploration of the relationships between work task and interactive information search behavior

This study explores the relationships between work task and interactive information search behavior. Work task was conceptualized based on a faceted classification of task. An experiment was conducted with six work-task types and simulated work-task ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2010

2690 pages

ISBN:9781605589299

DOI:10.1145/1753326

General Chair:
Elizabeth Mynatt
Georgia Institute of Technology
,
Program Chairs:
Geraldine Fitzpatrick
Vienna University of Technology
,
Scott Hudson
Carnegie Mellon University
,
Keith Edwards
Georgia Tech
,
Tom Rodden
University of Nottingham

Copyright © 2010 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]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 April 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI '10

Sponsor:

SIGCHI

CHI '10: CHI Conference on Human Factors in Computing Systems

April 10 - 15, 2010

Georgia, Atlanta, USA

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

181
Total Citations
View Citations
4,381
Total Downloads

Downloads (Last 12 months)254
Downloads (Last 6 weeks)12

Reflects downloads up to 23 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Vuong TJacucci GRuotsalo T(2024)Naturalistic Digital Behavior Predicts Cognitive AbilitiesACM Transactions on Computer-Human Interaction10.1145/366034131:3(1-32)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3660341
Kong HYadav SLowy RRuzinov DKim J(2024)Understanding Online Job and Housing Search Practices of Neurodiverse Young Adults to Support Their IndependenceProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642578(1-14)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642578
Smirnova A(2024)Syntactic Variation in Reduced Registers Through the Lens of the Parallel ArchitectureTopics in Cognitive Science10.1111/tops.12747Online publication date: 4-Jul-2024
https://doi.org/10.1111/tops.12747
Almansoori MIslam MGhosh SMondal MChatterjee R(2024)The Web of Abuse: A Comprehensive Analysis of Online Resource in the Context of Technology-Enabled Intimate Partner Surveillance2024 IEEE 9th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP60621.2024.00048(773-789)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSP60621.2024.00048
Botturi LHermida MAddimando LBeretta C(2024)Visualizing Online Search Processes for Information Literacy EducationInformation Experience and Information Literacy10.1007/978-3-031-52998-6_24(277-289)Online publication date: 1-Feb-2024
https://doi.org/10.1007/978-3-031-52998-6_24
Shah CWhite RThomas PMitra BSarkar SBelkin N(2023)Taking Search to TaskProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578288(1-13)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578288
Wang XLiu C(2023)Finding the Aha! Moment of Search: A Preliminary Examination of Insight Learning During SearchProceedings of the Association for Information Science and Technology10.1002/pra2.80060:1(421-432)Online publication date: 22-Oct-2023
https://doi.org/10.1002/pra2.800
Stone M(2022)Understanding and Evaluating Search ExperienceSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S01166ED1V01Y202202ICR07714:1(1-105)Online publication date: 28-Mar-2022
https://doi.org/10.2200/S01166ED1V01Y202202ICR077
Shah CWhite R(2021)Task Intelligence for Search and RecommendationSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S01103ED1V01Y202105ICR07413:3(1-160)Online publication date: 9-Jun-2021
https://doi.org/10.2200/S01103ED1V01Y202105ICR074
Martiniello NAsuncion JFichten CJorgensen MHavel AHarvison MLegault ALussier AVo C(2021)Artificial intelligence for students in postsecondary educationAI Matters10.1145/3446243.34462506:3(17-29)Online publication date: 10-Feb-2021
https://dl.acm.org/doi/10.1145/3446243.3446250
Show More Cited By

View Options

Get Access

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents