Eye tracking and online search: Lessons learned and challenges ahead

This article surveys the use of eye tracking in investigations of online search. Three eye tracking experiments that we undertook are discussed and compared to additional work in this area, revealing recurring behaviors and trends. The first two studies are described in greater detail in Granka, Joachims, & Gay (2004), Lorigo et al. (2006), and Pan et al. (2007), and the third study is described for the first time in this article. These studies reveal how users view the ranked results on a search engine results page (SERP), the relationship between the search result abstracts viewed and those clicked on, and whether gender, search task, or search engine influence these behaviors. In addition, we discuss a key challenge that arose in all three studies that applies to the use of eye tracking in studying online behaviors which is due to the limited support for analyzing scanpaths, or sequences of eye fixations. To meet this challenge, we present a preliminary approach that involves a graphical visualization to compare a path with a group of paths. We conclude by summarizing our findings and discussing future work in further understanding online search behavior with the help of eye tracking. © 2008 Wiley Periodicals, Inc.

[1]  E. Hess,et al.  Pupil Size as Related to Interest Value of Visual Stimuli , 1960, Science.

[2]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[3]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[4]  James N. MacGregor,et al.  Optimizing the Structure of Database Menu Indexes: A Decision Model of Menu Search , 1986 .

[5]  Ingrid Hsieh-Yee,et al.  Effects of Search Experience and Subject Knowledge on the Search Tactics of Novice and Experienced Searchers. , 1993 .

[6]  Joy Tillotson,et al.  Internet use through the University of Toronto Library: demographics, destinations, and users' reactions , 1995 .

[7]  Amanda Spink,et al.  Searching the Web: a survey of EXCITE users , 1999, Internet Res..

[8]  John R. Anderson,et al.  Eye tracking the visual search of click-down menus , 1999, CHI '99.

[9]  Amanda Spink,et al.  Real life, real users, and real needs: a study and analysis of user queries on the web , 2000, Inf. Process. Manag..

[10]  Robert W. Reeder,et al.  WebEyeMapper and WebLogger: tools for analyzing eye tracking data collected in web-use studies , 2001, CHI Extended Abstracts.

[11]  Bernard J. Jansen,et al.  A review of web searching studies and a framework for future research , 2001 .

[12]  Ingrid Hsieh-Yee.  Research on Web Search Behavior. , 2001 .

[13]  Dania Bilal,et al.  Differences and similarities in information seeking: children and adults as Web users , 2002, Inf. Process. Manag..

[14]  Andrew T Duchowski,et al.  A breadth-first survey of eye-tracking applications , 2002, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[15]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[16]  Michael E. Holmes,et al.  Visual attention to repeated internet images: testing the scanpath theory on the world wide web , 2002, ETRA.

[17]  Robert J. K. Jacob,et al.  Eye tracking in human-computer interaction and usability research : Ready to deliver the promises , 2002 .

[18]  Anthony J. Hornof,et al.  Cognitive Strategies for the Visual Search of Hierarchical Computer Displays , 2004, Hum. Comput. Interact..

[19]  Mark S. Ackerman,et al.  The perfect search engine is not enough: a study of orienteering behavior in directed search , 2004, CHI.

[20]  Thorsten Joachims,et al.  Eye-tracking analysis of user behavior in WWW search , 2004, SIGIR '04.

[21]  Elizabeth D. Liddy,et al.  The effects of expertise and feedback on search term selection and subsequent learning , 2005, J. Assoc. Inf. Sci. Technol..

[22]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[23]  Anne R. Haake,et al.  eyePatterns: software for identifying patterns and similarities across fixation sequences , 2006, ETRA.

[24]  Thorsten Joachims,et al.  The influence of task and gender on search and evaluation behavior using Google , 2006, Inf. Process. Manag..

[25]  Fabio Pellacini,et al.  Visualizing Paths in Context , 2006 .

[26]  Eric Brill,et al.  Improving web search ranking by incorporating user behavior information , 2006, SIGIR.

[27]  Kerry Rodden,et al.  Incorporating Eyetracking into User Studies at Google , 2006 .

[28]  Fred Stentiford,et al.  An eye tracking interface for image search , 2006, ETRA.

[29]  Edward Cutrell,et al.  An eye tracking study of the effect of target rank on web search , 2007, CHI.

[30]  Thorsten Joachims,et al.  In Google We Trust: Users' Decisions on Rank, Position, and Relevance , 2007, J. Comput. Mediat. Commun..

[31]  Susan T. Dumais,et al.  Improving Web Search Ranking by Incorporating User Behavior Information , 2019, SIGIR Forum.