Predicting whether users view dynamic content on the world wide web

Dynamic micro-content—interactive or updating widgets and features—is now widely used on the Web, but there is little understanding of how people allocate attention to it. In this article we present the results of an eye-tracking investigation examining how the nature of dynamic micro-content influences whether or not the user views it. We propose and validate the Dynamic Update Viewing-likelihood (DUV) model, a CHi-squared Automatic Interaction Detector (CHAID) model that predicts with around 80% accuracy whether users view dynamic updates as a function of how they are initiated, their size, and their duration. The model is constructed with data from live Web sites and does not rely on knowledge of the user's task to make its predictions, giving it a high level of external validity. We discuss one example of its application: informing how dynamic content should be presented in audio via assistive technology for people with visual impairments.

[1]  Michelle E. Bayles,et al.  Designing online banner advertisements: should we animate? , 2002, CHI.

[2]  Jan Theeuwes,et al.  Static items are automatically prioritized in a dynamic environment , 2008 .

[3]  Peter Marshall.  Doing Business on the Internet: Opportunities and Pitfalls , 2000 .

[4]  Thomas S. Tullis,et al.  Generation Y, web design, and eye tracking , 2010, Int. J. Hum. Comput. Stud..

[5]  Robert Stevens,et al.  How people use presentation to search for a link: expanding the understanding of accessibility on the Web , 2006, W4A '06.

[6]  Tovi Grossman,et al.  Ambient help , 2011, CHI.

[7]  K. Gegenfurtner,et al.  Design Issues in Gaze Guidance Under review with ACM Transactions on Computer Human Interaction , 2009 .

[8]  Craig S. Miller,et al.  Modeling Information Navigation: Implications for Information Architecture , 2004, Hum. Comput. Interact..

[9]  Andy Brown,et al.  Web 2.0: An accessibility-oriented survey of dynamic updates , 2010 .

[10]  Marilyn Hughes Blackmon,et al.  Cognitive Architecture for Website Design and Usability Evaluation : Comprehension and Information Scent in Performing by Exploration , 2005 .

[11]  Paul van Schaik,et al.  The effects of graphical display and screen ratio on information retrieval in web pages , 2006, Comput. Hum. Behav..

[12]  Caroline Jay,et al.  Tailored presentation of dynamic web content for audio browsers , 2012, Int. J. Hum. Comput. Stud..

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

[14]  Ishani Chakraborty,et al.  Correlating low-level image statistics with users - rapid aesthetic and affective judgments of web pages , 2009, CHI.

[15]  Anthony J. Hornof,et al.  A minimal model for predicting visual search in human-computer interaction , 2007, CHI.

[16]  Muneo Kitajima,et al.  The influence of web browsing experience on web-viewing behavior , 2006, ETRA '06.

[17]  S. Yantis,et al.  Visual motion and attentional capture , 1994, Perception & psychophysics.

[18]  D. Simons,et al.  Moving and looming stimuli capture attention , 2003, Perception & psychophysics.

[19]  L. Itti,et al.  Visual causes versus correlates of attentional selection in dynamic scenes , 2006, Vision Research.

[20]  Andy Brown,et al.  Using qualitative eye-tracking data to inform audio presentation of dynamic Web content , 2010, New Rev. Hypermedia Multim..

[21]  Su-Ling Yeh,et al.  New objects do not capture attention without a top-down setting: Evidence from an inattentional blindness task , 2007 .

[22]  Fay Sudweeks,et al.  Doing Business on the Internet: Opportunities and Pitfalls , 2012 .

[23]  Kerry Rodden,et al.  Eye-mouse coordination patterns on web search results pages , 2008, CHI Extended Abstracts.

[24]  M. Potter,et al.  Recognition memory for a rapid sequence of pictures. , 1969, Journal of experimental psychology.

[25]  Heike Schaumburg,et al.  Why Are Users Banner-Blind? The Impact of Navigation Style on the Perception of Web Banners , 2006, J. Digit. Inf..

[26]  Thomas W. Calvert,et al.  Moticons: : detection, distraction and task , 2003, Int. J. Hum. Comput. Stud..

[27]  John T. Stasko,et al.  Establishing tradeoffs that leverage attention for utility: empirically evaluating information display in notification systems , 2003, Int. J. Hum. Comput. Stud..

[28]  Wai-Tat Fu,et al.  SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web , 2007, Hum. Comput. Interact..

[29]  Geri Gay,et al.  Location location location: viewing patterns on WWW pages , 2006, ETRA '06.

[30]  Andy Brown,et al.  Audio presentation of auto-suggest lists , 2009, W4A.

[31]  G. Boynton,et al.  Global feature-based attention for motion and color , 2003, Vision Research.

[32]  Paul P. Maglio,et al.  Tradeoffs in displaying peripheral information , 2000, CHI.

[33]  Dan Diaper,et al.  World Wide Web working whilst ignoring graphics: good news for web page designers , 2000, Interact. Comput..

[34]  Andy Brown,et al.  Audio access to calendars , 2010, W4A.

[35]  Anthony J. Hornof,et al.  High-cost banner blindness: Ads increase perceived workload, hinder visual search, and are forgotten , 2005, TCHI.

[36]  J Miller,et al.  The control of attention by abrupt visual onsets and offsets , 1989, Perception & psychophysics.

[37]  Vanessa Beanland,et al.  Looking without seeing or seeing without looking? Eye movements in sustained inattentional blindness , 2010, Vision Research.

[38]  HarperSimon,et al.  Predicting whether users view dynamic content on the world wide web , 2013 .

[39]  Bonnie E. John,et al.  The Evolution of a Goal-Directed Exploration Model: Effects of Information Scent and GoBack Utility on Successful Exploration , 2011, Top. Cogn. Sci..