Measuring User Engagement

User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus ``in the wild''), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users' motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings, and across different user groups and web domains. At the same time, this book advocates for the development of ``good'' measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct, which has become so vital in our wired world. Table of Contents: Preface / Acknowledgments / Introduction and Scope / Approaches Based on Self-Report Methods / Approaches Based on Physiological Measurements / Approaches Based on Web Analytics / Beyond Desktop, Single Site, and Single Task / Enhancing the Rigor of User Engagement Methods and Measures / Conclusions and Future Research Directions / Bibliography / Authors' Biographies / Index

[1]  J. Read,et al.  Endurability, Engagement and Expectations: Measuring Children’s Fun , 2002 .

[2]  Fabrizio Silvestri,et al.  Identifying task-based sessions in search engine query logs , 2011, WSDM '11.

[3]  Elaine Toms,et al.  Understanding and facilitating the browsing of electronic text , 2000, Int. J. Hum. Comput. Stud..

[4]  Andreas Dengel,et al.  Query expansion using gaze-based feedback on the subdocument level , 2008, SIGIR '08.

[5]  Lloyd P. Rieber,et al.  Seriously considering play: Designing interactive learning environments based on the blending of microworlds, simulations, and games , 1996 .

[6]  Jennifer L Branch Investigating the Information-Seeking Processes of Adolescents: The Value of Using Think Alouds and Think Afters , 2000 .

[7]  Yudhijit Bhattacharjee,et al.  Measuring the immeasurable , 2001, Nature.

[8]  Chao Chen,et al.  System-wide support for safety in pervasive spaces , 2011, Journal of Ambient Intelligence and Humanized Computing.

[9]  Eugene Agichtein,et al.  Discovering common motifs in cursor movement data for improving web search , 2014, WSDM.

[10]  Eugene Agichtein,et al.  ViewSer: enabling large-scale remote user studies of web search examination and interaction , 2011, SIGIR.

[11]  Christine T. Kydd,et al.  Individual characteristics associated with World Wide Web use: an empirical study of playfulness and motivation , 1997, DATB.

[12]  Mike Thelwall,et al.  Sentiment strength detection for the social web , 2012, J. Assoc. Inf. Sci. Technol..

[13]  Susanne Bødker,et al.  When second wave HCI meets third wave challenges , 2006, NordiCHI '06.

[14]  David V. Keyson,et al.  Product features and task effects on experienced richness, control and engagement in voicemail browsing , 2009, Personal and Ubiquitous Computing.

[15]  Padmini Srinivasan,et al.  Quality through flow and immersion: gamifying crowdsourced relevance assessments , 2012, SIGIR '12.

[16]  S. Levinson,et al.  WEIRD languages have misled us, too , 2010, Behavioral and Brain Sciences.

[17]  T. D. Wilson,et al.  Review of: Berg, Bruce L. Qualitative research methods for the social sciences, 6th ed. Boston, MA: Allyn and Bacon, 2007 , 2008, Inf. Res..

[18]  Mark D. Smucker,et al.  Mouse movement during relevance judging: implications for determining user attention , 2014, SIGIR.

[19]  J. C. Flanagan Psychological Bulletin THE CRITICAL INCIDENT TECHNIQUE , 2022 .

[20]  Robin L. Wakefield,et al.  Mobile computing: a user study on hedonic/utilitarian mobile device usage , 2006, Eur. J. Inf. Syst..

[21]  A. Burton-Jones Minimizing Method Bias Through Programmatic Research , 2009 .

[22]  Mark Claypool,et al.  Implicit interest indicators , 2001, IUI '01.

[23]  Joemon M. Jose,et al.  User engagement in online News: Under the scope of sentiment, interest, affect, and gaze , 2014, J. Assoc. Inf. Sci. Technol..

[24]  N. Tractinsky,et al.  What is beautiful is usable , 2000, Interact. Comput..

[25]  Andrea Lockerd Thomaz,et al.  Cheese: tracking mouse movement activity on websites, a tool for user modeling , 2001, CHI Extended Abstracts.

[26]  Frank E. Pollick,et al.  Understanding Relevance: An fMRI Study , 2013, ECIR.

[27]  Eugene Agichtein,et al.  Ready to buy or just browsing?: detecting web searcher goals from interaction data , 2010, SIGIR.

[28]  Ryen W. White,et al.  User see, user point: gaze and cursor alignment in web search , 2012, CHI.

[29]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[30]  Gabriella Kazai,et al.  Model for Voter Scoring and Best Answer Selection in Community Q&A Services , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[31]  Takahiro Hara,et al.  How Does Mobile Context Affect People's Web Search Behavior?: A Diary Study of Mobile Information Needs and Search Behaviors , 2012, 2012 IEEE 26th International Conference on Advanced Information Networking and Applications.

[32]  Lik Mui,et al.  A Computational Model of Trust and Reputation for E-businesses , 2002 .

[33]  R. Downey,et al.  Rating the ratings: Assessing the psychometric quality of rating data , 1980 .

[34]  Jan C. Frijters,et al.  A Review of Self-Report and Alternative Approaches in the Measurement of Student Motivation , 2009 .

[35]  Timoleon Wilkins At This Moment , 2012 .

[36]  Diane Kelly,et al.  Questionnaire mode effects in interactive information retrieval experiments , 2008, Inf. Process. Manag..

[37]  Chih-Hung Hsieh,et al.  Towards better measurement of attention and satisfaction in mobile search , 2014, SIGIR.

[38]  J. H. Frey,et al.  The Interview: From Neutral Stance to Political Involvement. , 2005 .

[39]  Mounia Lalmas,et al.  On saliency, affect and focused attention , 2012, CHI.

[40]  Ricardo Baeza-Yates,et al.  Online multitasking and user engagement , 2013, CIKM.

[41]  Albert H. Segars,et al.  Exploring the theoretical foundations of playfulness in computer interactions , 2002, Comput. Hum. Behav..

[42]  Jacek Gwizdka,et al.  Characterizing relevance with eye-tracking measures , 2014, IIiX.

[43]  Reynol Junco,et al.  Comparing actual and self-reported measures of Facebook use , 2013, Comput. Hum. Behav..

[44]  Brad Feld,et al.  Trying New Things , 2012 .

[45]  Scott Counts,et al.  Taking It All In? Visual Attention in Microblog Consumption , 2021, ICWSM.

[46]  Eugene Agichtein,et al.  Towards predicting web searcher gaze position from mouse movements , 2010, CHI Extended Abstracts.

[47]  Ryen W. White,et al.  A study on the effects of personalization and task information on implicit feedback performance , 2006, CIKM '06.

[48]  Ravi Kumar,et al.  A characterization of online browsing behavior , 2010, WWW '10.

[49]  Stephen H. Fairclough,et al.  A research agenda for physiological computing , 2004, Interact. Comput..

[50]  Jaime Teevan,et al.  Understanding the importance of location, time, and people in mobile local search behavior , 2011, Mobile HCI.

[51]  B. Berg Qualitative Research Methods for the Social Sciences , 1989 .

[52]  Amanda Spink,et al.  Multitasking during Web search sessions , 2006, Inf. Process. Manag..

[53]  Peter Chapman,et al.  Engagement in multimedia training systems , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[54]  O. Aalen,et al.  Survival and Event History Analysis: A Process Point of View , 2008 .

[55]  Mounia Lalmas,et al.  Absence time and user engagement: evaluating ranking functions , 2013, WSDM '13.

[56]  Andy Cockburn,et al.  An empirical analysis of web page revisitation , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[57]  Hilary Hutchinson,et al.  Measuring the user experience on a large scale: user-centered metrics for web applications , 2010, CHI.

[58]  Virpi Roto,et al.  Understanding, scoping and defining user experience: a survey approach , 2009, CHI.

[59]  J. Findlay,et al.  The Relationship between Eye Movements and Spatial Attention , 1986, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[60]  Thomas A. Farmer,et al.  Hand in Motion Reveals Mind in Motion , 2011, Front. Psychology.

[61]  David Elliott,et al.  In the Wild , 2010 .

[62]  P. Tsang,et al.  Diagnosticity and multidimensional subjective workload ratings. , 1996, Ergonomics.

[63]  R. Remington Attention and saccadic eye movements. , 1980, Journal of experimental psychology. Human perception and performance.

[64]  Randy F. Pausch,et al.  What HCI designers can learn from video game designers , 1994, CHI Conference Companion.

[65]  Maria João da Costa Pereira,et al.  Mihaly Csikszentmihalyi. 2002. Fluir: A Psicologia da Experiência Óptima. Título original: Flow: The Psychology of Optimal Experience (1990). Nova Iorque: Harper Perennial. Tradução de Marta Amado. Lisboa: Relógio d’Água Editores , 2002 .

[66]  Qing Wang,et al.  Multitasking bar: prototype and evaluation of introducing the task concept into a browser , 2010, CHI.

[67]  Antti Pirhonen,et al.  Future Interaction Design , 2010 .

[68]  Ryen W. White,et al.  Modeling dwell time to predict click-level satisfaction , 2014, WSDM.

[69]  Ioannis Arapakis,et al.  Theories, methods and current research on emotions in library and information science, information retrieval and human-computer interaction , 2011, Inf. Process. Manag..

[70]  Elizabeth F. Churchill,et al.  Mouse tracking: measuring and predicting users' experience of web-based content , 2012, CHI.

[71]  Ron Kohavi,et al.  Trustworthy online controlled experiments: five puzzling outcomes explained , 2012, KDD.

[72]  G. A. Mendelsohn,et al.  Affect grid : A single-item scale of pleasure and arousal , 1989 .

[73]  Eelco Herder Characterizations of User Web Revisit Behavior , 2005, LWA.

[74]  M H Fischer,et al.  An Investigation of Attention Allocation during Sequential Eye Movement Tasks , 1999, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[75]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[76]  Noam Tractinsky,et al.  Assessing dimensions of perceived visual aesthetics of web sites , 2004 .

[77]  ThelwallMike,et al.  Sentiment strength detection for the social web , 2012 .

[78]  P. Lang The emotion probe. Studies of motivation and attention. , 1995, The American psychologist.

[79]  Siddharth Suri,et al.  Conducting behavioral research on Amazon’s Mechanical Turk , 2010, Behavior research methods.

[80]  Adam Rifkin,et al.  Trust Management on the World Wide Web , 1998, Comput. Networks.

[81]  Gabriella Kazai,et al.  Towards a science of user engagement (Position Paper) , 2011 .

[82]  Daniel Callison,et al.  Time on Task. , 1998 .

[83]  Alexander J. Smola,et al.  Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts , 2013, WWW.

[84]  K. A. Ericsson,et al.  Protocol Analysis: Verbal Reports as Data , 1984 .

[85]  Ricardo Baeza-Yates,et al.  The effect of links on networked user engagement , 2012, WWW.

[86]  Ryen W. White,et al.  Search, interrupted: understanding and predicting search task continuation , 2012, SIGIR '12.

[87]  John R. Anderson,et al.  What can a mouse cursor tell us more?: correlation of eye/mouse movements on web browsing , 2001, CHI Extended Abstracts.

[88]  Elaine Toms,et al.  Is there a universal instrument for measuring interactive information retrieval?: the case of the user engagement scale , 2010, IIiX.

[89]  Gabriella Kazai,et al.  Effects of Social Approval Votes on Search Performance , 2009, 2009 Sixth International Conference on Information Technology: New Generations.

[90]  Alistair Sutcliffe,et al.  Designing for User Engagement: Aesthetic and Attractive User Interfaces , 2009, Designing for User Engagement.

[91]  Gitte Lindgaard,et al.  Attention web designers: You have 50 milliseconds to make a good first impression! , 2006, Behav. Inf. Technol..

[92]  Eugene Agichtein,et al.  Predicting web search success with fine-grained interaction data , 2012, CIKM.

[93]  Rene Arcilla True to Life , 2012 .

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

[95]  Eugene Agichtein,et al.  Exploring mouse movements for inferring query intent , 2008, SIGIR '08.

[96]  Yongjun Sung,et al.  To App or Not to App: Engaging Consumers via Branded Mobile Apps , 2013 .

[97]  K. A. Ericsson,et al.  Verbal reports as data. , 1980 .

[98]  Andreas Dengel,et al.  Eye tracking analysis of preferred reading regions on the screen , 2010, CHI Extended Abstracts.

[99]  Jude W. Shavlik,et al.  Learning users' interests by unobtrusively observing their normal behavior , 2000, IUI '00.

[100]  Mounia Lalmas,et al.  An Exploration of Cursor tracking Data , 2015, ArXiv.

[101]  K. A. Ericsson,et al.  Protocol Analysis and Expert Thought: Concurrent Verbalizations of Thinking during Experts' Performance on Representative Tasks , 2006 .

[102]  Antti Oulasvirta,et al.  Habits make smartphone use more pervasive , 2011, Personal and Ubiquitous Computing.

[103]  J. Antonio Hyder Proposal of a web site engagement scale and research model. Analysis of the influence of intra web site comparative behaviour , 2011 .

[104]  Caroline Hummels,et al.  Let's Make Things Engaging , 2005, Funology.

[105]  Stephen H. Fairclough,et al.  Fundamentals of physiological computing , 2009, Interact. Comput..

[106]  Elena Karahanna,et al.  Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..

[107]  Elizabeth F. Churchill Enticing engagement , 2010, INTR.

[108]  Virgílio A. F. Almeida,et al.  Characterizing user behavior in online social networks , 2009, IMC '09.

[109]  Diane Kelly,et al.  Methods for Evaluating Interactive Information Retrieval Systems with Users , 2009, Found. Trends Inf. Retr..

[110]  Ryen W. White,et al.  Large-scale analysis of individual and task differences in search result page examination strategies , 2012, WSDM '12.

[111]  Steve Jackson Cult of Analytics: Driving online marketing strategies using web analytics , 2009 .

[112]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[113]  Steve Fox,et al.  Evaluating implicit measures to improve web search , 2005, TOIS.

[114]  Tapas Kanungo,et al.  On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals , 2011, WSDM '11.

[115]  Ilpo Koskinen,et al.  Co-experience: user experience as interaction , 2005 .

[116]  E. Krahmer,et al.  Thinking about thinking aloud: a comparison of two verbal protocols for usability testing , 2004, IEEE Transactions on Professional Communication.

[117]  Heather L. O'Brien,et al.  The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences , 2010, Interact. Comput..

[118]  Ryen W. White,et al.  Mining the search trails of surfing crowds: identifying relevant websites from user activity , 2008, WWW.

[119]  D. Watson,et al.  Development and validation of brief measures of positive and negative affect: the PANAS scales. , 1988, Journal of personality and social psychology.

[120]  Joemon M. Jose,et al.  A comparison of general vs personalised affective models for the prediction of topical relevance , 2010, SIGIR '10.

[121]  Cliff Lampe,et al.  The Benefits of Facebook "Friends: " Social Capital and College Students' Use of Online Social Network Sites , 2007, J. Comput. Mediat. Commun..

[122]  Mark C. Fox,et al.  Do procedures for verbal reporting of thinking have to be reactive? A meta-analysis and recommendations for best reporting methods. , 2011, Psychological bulletin.

[123]  Michael J. Albers,et al.  Content and Complexity: Information Design in Technical Communication , 2002 .

[124]  Ahmad Rahmati,et al.  Studying Smartphone Usage: Lessons from a Four-Month Field Study , 2013, IEEE Transactions on Mobile Computing.

[125]  Mounia Lalmas,et al.  Automatically embedding newsworthy links to articles: From implementation to evaluation , 2014, J. Assoc. Inf. Sci. Technol..

[126]  Ricardo Baeza-Yates,et al.  Measuring inter-site engagement , 2013, 2013 IEEE International Conference on Big Data.

[127]  Edward Cutrell,et al.  What are you looking for?: an eye-tracking study of information usage in web search , 2007, CHI.

[128]  B. Velichkovsky,et al.  On the control of visual fixation durations in free viewing of complex images , 2011, Attention, perception & psychophysics.

[129]  Constantine Stephanidis,et al.  HCI International 2013 - Posters’ Extended Abstracts , 2013, Communications in Computer and Information Science.

[130]  M. Csíkszentmihályi,et al.  The Experience Sampling Method , 2014 .

[131]  George Valkanas,et al.  Understanding Within-Content Engagement through Pattern Analysis of Mouse Gestures , 2014, CIKM.

[132]  C. B. Colby The weirdest people in the world , 1973 .

[133]  Michael A. Shepherd,et al.  A Field Study Characterizing Web-based Information Seeking Tasks , 2022 .

[134]  Ricardo Baeza-Yates,et al.  Networked user engagement , 2013, UEO '13.

[135]  Eelco Herder,et al.  Web page revisitation revisited: implications of a long-term click-stream study of browser usage , 2007, CHI.

[136]  Thomas E. Nygren,et al.  The Subjective Workload Assessment Technique: A Scaling Procedure for Measuring Mental Workload , 1988 .

[137]  Fatemeh Zahedi,et al.  The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach , 2002, Inf. Syst. Res..

[138]  Yang Song,et al.  Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance , 2013, WWW '13.

[139]  Heather L. O'Brien,et al.  Mixed-methods approach to measuring user experience in online news interactions , 2013, J. Assoc. Inf. Sci. Technol..

[140]  Barry Smyth,et al.  Understanding the intent behind mobile information needs , 2009, IUI.

[141]  D. Tojib,et al.  Post-adoption modeling of advanced mobile service use , 2012 .

[142]  R. Plutchik,et al.  Emotion: Theory, Research, and Experience. Vol. 1. Theories of Emotion , 1981 .

[143]  Virpi Roto,et al.  User experience evaluation: do you know which method to use? , 2009, CHI Extended Abstracts.

[144]  R. Craig Lefebvre,et al.  The Assessment of User Engagement with eHealth Content: The eHealth Engagement Scale , 2010, J. Comput. Mediat. Commun..

[145]  J. Henderson Human gaze control during real-world scene perception , 2003, Trends in Cognitive Sciences.

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

[147]  Mounia Lalmas,et al.  Models of user engagement , 2012, UMAP.

[148]  Yoichi Shinoda,et al.  Information filtering based on user behavior analysis and best match text retrieval , 1994, SIGIR '94.

[149]  Daniel Baldauf,et al.  Time perception as a workload measure in simulated car driving. , 2009, Applied ergonomics.

[150]  Susana Rubio,et al.  Evaluation of Subjective Mental Workload: A Comparison of SWAT, NASA‐TLX, and Workload Profile Methods , 2004 .

[151]  Virpi Roto,et al.  Interaction in 4-second bursts: the fragmented nature of attentional resources in mobile HCI , 2005, CHI.

[152]  Joseph J. Martocchio,et al.  Microcomputer playfulness: development of a measure with workplace implications , 1992 .

[153]  Daniel M. Johnson,et al.  Frictional widgets: enhancing touch interfaces with programmable friction , 2011, CHI EA '11.

[154]  Bracha Shapira,et al.  Study of the usefulness of known and new implicit indicators and their optimal combination for accurate inference of users interests , 2006, SAC.

[155]  D. Sculley,et al.  Predicting bounce rates in sponsored search advertisements , 2009, KDD.

[156]  Ryen W. White,et al.  No clicks, no problem: using cursor movements to understand and improve search , 2011, CHI.

[157]  Nicholas J. Belkin,et al.  Display time as implicit feedback: understanding task effects , 2004, SIGIR '04.

[158]  Elizabeth D. Murphy,et al.  Think-aloud protocols: a comparison of three think-aloud protocols for use in testing data-dissemination web sites for usability , 2010, CHI.

[159]  Eugene Agichtein,et al.  Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior , 2012, WWW.

[160]  R. Peterson Constructing Effective Questionnaires , 1999 .

[161]  Stefano Mizzaro,et al.  Relevance: The Whole History , 1997, J. Am. Soc. Inf. Sci..

[162]  Nuria Oliver,et al.  Understanding mobile web and mobile search use in today's dynamic mobile landscape , 2011, Mobile HCI.

[163]  Vyas Sekar,et al.  Understanding the impact of video quality on user engagement , 2011, SIGCOMM.

[164]  Morgan Jennings,et al.  Theory and models for creating engaging and immersive ecommerce Websites , 2000, SIGCPR '00.

[165]  Wijnand A. IJsselsteijn,et al.  Presence: concept, determinants, and measurement , 2000, Electronic Imaging.

[166]  Paul A. Cairns,et al.  Measuring and defining the experience of immersion in games , 2008, Int. J. Hum. Comput. Stud..

[167]  Jane Webster,et al.  Audience engagement in multimedia presentations , 1997, DATB.

[168]  Firdaus Banhawi,et al.  Measuring user engagement attributes in social networking application , 2011, 2011 International Conference on Semantic Technology and Information Retrieval.

[169]  Jane Webster,et al.  Enhancing the Design of Web Navigation Systems: The Influence of User Disorientation on Engagement and Performance , 2006, MIS Q..

[170]  Ravi Kumar,et al.  Attention and selection in online choice tasks , 2012, UMAP.

[171]  Dan Morris,et al.  Instrumenting the Dynamic Web , 2007, J. Web Eng..

[172]  Stina Nylander,et al.  "It's Just Easier with the Phone" - A Diary Study of Internet Access from Cell Phones , 2009, Pervasive.

[173]  Johann Schrammel,et al.  3D attention: measurement of visual saliency using eye tracking glasses , 2013, CHI Extended Abstracts.

[174]  Elaine Toms,et al.  The development and evaluation of a survey to measure user engagement , 2010, J. Assoc. Inf. Sci. Technol..

[175]  K. Nakayama,et al.  Hidden cognitive states revealed in choice reaching tasks , 2009, Trends in Cognitive Sciences.

[176]  Jeffrey Boase,et al.  No Such Effect? The Implications of Measurement Error in Self-Report Measures of Mobile Communication Use , 2012 .

[177]  Bernardo A. Huberman,et al.  The Pulse of News in Social Media: Forecasting Popularity , 2012, ICWSM.

[178]  Elaine Toms,et al.  Examining the generalizability of the User Engagement Scale (UES) in exploratory search , 2013, Inf. Process. Manag..

[179]  Giuseppe Riva,et al.  Why Is Facebook So Successful? Psychophysiological Measures Describe a Core Flow State While Using Facebook , 2011, Cyberpsychology Behav. Soc. Netw..

[180]  Duncan J. Watts,et al.  Financial incentives and the "performance of crowds" , 2009, HCOMP '09.

[181]  Fernando Diaz,et al.  Robust models of mouse movement on dynamic web search results pages , 2013, CIKM.

[182]  Marc Hassenzahl,et al.  User experience - a research agenda , 2006, Behav. Inf. Technol..

[183]  James D. Hollan,et al.  Direct Manipulation Interfaces , 1985, Hum. Comput. Interact..

[184]  Norma S. Said,et al.  An engaging multimedia design model , 2004, IDC '04.

[185]  Suju Rajan,et al.  Beyond clicks: dwell time for personalization , 2014, RecSys '14.

[186]  Veranika Lim,et al.  Eye Gaze and Mouse Cursor Relationship in a Debugging Task , 2013, HCI.

[187]  Mimi Recker,et al.  Using web metrics to analyze digital libraries , 2008, JCDL.

[188]  Matthew Richardson,et al.  Predicting clicks: estimating the click-through rate for new ads , 2007, WWW '07.

[189]  Elaine Toms,et al.  What is user engagement? A conceptual framework for defining user engagement with technology , 2008, J. Assoc. Inf. Sci. Technol..

[190]  Susan T. Dumais,et al.  The good, the bad, and the random: an eye-tracking study of ad quality in web search , 2010, SIGIR.

[191]  Ryen W. White,et al.  Parallel browsing behavior on the web , 2010, HT '10.

[192]  Brenda Laurel,et al.  Computers as theatre , 1991 .

[193]  K. Rayner Eye movements and visual cognition : scene perception and reading , 1992 .

[194]  Henry J. Gardner,et al.  Engagement networks in social music-making , 2010, OZCHI '10.

[195]  Paramvir Bahl,et al.  Analyzing the browse patterns of mobile clients , 2001, IMW '01.

[196]  Jennifer Golbeck,et al.  Weaving a Web of Trust , 2008, Science.

[197]  S. Iliffe,et al.  Bmc Medical Research Methodology Open Access the Hawthorne Effect: a Randomised, Controlled Trial , 2022 .

[198]  Nicholas J. Belkin,et al.  Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback , 2001, Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.

[199]  Rafa Absar,et al.  Toward a Model of Mobile User Engagement , 2013 .

[200]  Bernard J. Jansen,et al.  Evaluating the effectiveness of and patterns of interactions with automated searching assistance , 2005, J. Assoc. Inf. Sci. Technol..

[201]  Richard David Jacques,et al.  The nature of engagement and its role in hypermedia evaluation and design , 1996 .

[202]  Jane Webster,et al.  Perceived disorientation: an examination of a new measure to assess web design effectiveness , 2001, Interact. Comput..

[203]  Luca Longo,et al.  Human-Computer Interaction and Human Mental Workload: Assessing Cognitive Engagement in the World Wide Web , 2011, INTERACT.

[204]  R.I.A. Mercuri,et al.  Technology as Experience , 2005, IEEE Transactions on Professional Communication.

[205]  Ayse Göker,et al.  Evaluation of a mobile information system in context , 2008, Inf. Process. Manag..

[206]  Menno D. T. de Jong,et al.  Retrospective vs. concurrent think-aloud protocols: Testing the usability of an online library catalogue , 2003, Behav. Inf. Technol..

[207]  Hila Becker,et al.  What happens after an ad click?: quantifying the impact of landing pages in web advertising , 2009, CIKM.

[208]  Liam J. Bannon,et al.  A human-centred perspective on interaction design , 2005 .

[209]  James E. Pitkow,et al.  Characterizing Browsing Strategies in the World-Wide Web , 1995, Comput. Networks ISDN Syst..

[210]  Shumeet Baluja,et al.  A large scale study of wireless search behavior: Google mobile search , 2006, CHI.

[211]  Evangelos Karapanos,et al.  Theories, methods and case studies of longitudinal HCI research , 2012, CHI Extended Abstracts.