The document discusses two studies on measuring user engagement: 1. Models of user engagement - Developed models of engagement using clustering and metrics like popularity, activity, loyalty across user groups and time periods. Multiple independent models were found. 2. Saliency, attention and affect - Examined how visual saliency impacts engagement by manipulating page saliency. It measured effects on focused attention, positive/negative affect, and interest using tasks and questionnaires.

1. User Engagement – A Scientific Challenge Mounia Lalmas Yahoo! Research Barcelona [email_address] Ioannis Arapakis Ricardo Baeza-Yates Georges Dupret Janette Lehmann Lori McCay-Peet Vidhya Navalpakkam Elad Yom-Tov Collaborators

2. Outline Motivation and Definition Characteristics of User Engagement Current measurements Vision and focus Two “opposite” studies Models of user engagement Saliency and user engagement

3. Motivation User engagement is a quality of user experience that emphasizes the positive aspects of interaction – in particular the fact of being captivated by the technology. Successful technologies are not just used, they are engaged with . A web interface that is boring, a multimedia presentation that does not captivate users’ attention or an online forum that fails to engender a sense of community are quickly dismissed with a simple mouse click. (O’Brian and Toms, 2008) User engagement is how we nurture and build a community. (John Byrne, Business Weeks’ online editor 2009)

4. Would a user engage with this web site? http://www.nhm.ac.uk/

5. Would a user engage with this web site? http://www.amazingthings.org/ (art event calendar)

6. Would a user engage with this web site? http://www.lowpriceskates.com/ (e-commerce – skating)

7. Would a user engage with this web site? http://chiptune.com/ (music repository)

8. Would a user engage with this web site? http://www.theosbrinkagency.com/ (photographer)

9. What is user engagement? “ The emotional, cognitive and/or behavioural connection that exists, at any point in time and over time, between a user and a technological resource” “ Newish” field of research stemming from the increasing interaction between users and web services and technologies Current work focuses on two aspects What are the characteristics of user engagement? How can we measure user engagement? Subjective measures Objective measures

10. Characteristics of user engagement (I)

11. Characteristics of user engagement (II)

12. Forrester Research – The four I’s Measuring Engagement, Forrester Research, June 2008

13. Peterson etal Engagement measure – 8 indices Click Depth Index: page views Duration Index : time spent Recency Index: rate at which users return over time Loyalty Index: level of long-term interaction the user has with the site or product (frequency) Brand Index: apparent awareness of the user of the brand, site, or product (search terms) Feedback Index: qualitative information including propensity to solicit additional information or supply direct feedback Interaction Index: user interaction with site or product (click, upload, transaction) Peterson etal. Measuring the immeasurable: visitor engagement, WebAnalyticsDemystified, September 2008

14. Subjective measures Record a user’s perception (generally self-reported) of the technology Two-part processes: Increased use of crowd-sourcing based studies

15. Objective measures To overcome the subjectivity of post-experience questionnaires To measure engagement over large populations and over time

16. Objective measures – Online activities Proxy of user engagement bounce rates, CTR, days since last visit, exit rate, entrance rate, frequency, number of new visitors, number of visitors, number of returning visitors, number of bookmarks (e.g. on Delicious), number of comments posted, number of content syndication (RSS), content contribution (adding a comment, adding a review, rating, uploading an image or video), number of repeated visitors, page views, time spent (dwell time), visits per visitors, number of tags (e.g. on Flickr), number of emailed and printed stories, number of Facebook likes, number of re-tweets, number of messages sent (instant, email), number of conversions (e.g. subscribing, buying), number of Facebook fan pages, number of search queries, common paths (e.g. from front page to mail tool then exit), external comments and reviews (for products), etc …

17. Characteristics and measures S. Attfield, G. Kazai, M. Lalmas and B. Piwowarski. Towards a science of user engagement (Position Paper), WSDM Workshop on User Modelling for Web Applications, 2011.

18. Diagnostic and what we can do Diagnostic: work exists, but fragmented. In particular: What and how to measure depend on services and goals Lack of understanding of how to relate subjective and objective measures What I (we) have done: “ Towards” Models of user engagement Stylistics and “engagement” Saliency, interest, attention and positive affect Front page styles and downstream engagement Automatic linking and reading experience

19. Page stylistics + layout +links + saliency Measurements and methodologies + online analytics metrics (dwell time, …) + questionnaires + crowd-sourcing - biometrics (eye tracking, mouse tracking, …) Goals + Models of user engagement - Metrics of user engagement Engagement types + attention + emotion + activity + popularity + loyalty - functionality +/- intent & interest user engagement within and across site

20. Focus – Two “opposite” studies models USER ENGAGEMENT 1. Models of user engagement 2. On saliency, attention and positive effect activity metrics loyalty metrics popularity metrics hobbies navigation social media e-commerce magazine sport news search weather mail weekly news … focused attention affect (emotion) saliency

21. 1. Models of user engagement User engagement characteristics depend on the web application: e.g. reading mail or browsing a news portal results in different types of engagement Diversity of engagement Models of engagement through clustering using various criteria e.g. user types, temporal aspects

22. Data and Metrics Interaction data, 2M users, July 2011, 73 US sites Popularity #Users Number of distinct users #Visits Number of visits #Clicks Number of clicks Activity ClickDepth Average number of page views per visit. DwellTimeA Average time per visit Loyalty ActiveDays Number of days a user visited the site ReturnRate Number of times a user visited the site DwellTimeL Average time a user spend on the site.

23. Diversity in user engagement Users and Loyalty Sites have different user groups Proportion of user groups is site-dependent Time and Popularity Site engagement can be periodic or contains peaks Engagement of a site depends on users and time mail, social media shopping, entertainment media (special events) daily activity, navigation media, entertainment

24. Methodology General models User-based models Time-based models Dimensions 8 metrics 5 user groups 8 metrics per user group weekdays, weekend 8 metrics per time span #Dimensions 8 40 16 Kernel k-means with Kendall tau rank correlation kernel Nb of clusters based on eigenvalue distribution of kernel matrix Significant metric values with Kruskal-Wallis/Bonferonni #Clusters (Models) 6 7 5 Analysing cluster centroids = models

25. Models of user engagement 6 general models Popularity, activity and loyalty are independent from each other Popularity and loyalty are influenced by external and internal factors e.g. frequency of publishing new information, events, personal interests Activity depends on the structure of the site Models based on engagement metrics interest-specific e-commerce, configuration periodic media

26. Models of user engagement User-based [7 models] Models based on engagement per user group Time-based [5 models] Models based on engagement over weekdays and weekend Models based on engagement metrics, user and time navigation game, sport hobbies, interest-specific daily news Sites of the same type (e.g. mainstream media) do not necessarily belong to the same model The groups of models describe different aspects of engagement, i.e. they are independent from each other

27. Relationships between models Groups of models are independent from each other Example: Model m u2 [high popularity and activity in all user groups, increasing loyalty] 50% to model m t2 [ high popularity on weekends and high loyalty on weekdays ] 50% to model m t3 [ high activity and loyalty on weekends ] Variance of Information [0,5.61] General User Time General 0.00 3.50 4.23 User 3.50 0.00 4.25 Time 4.23 4.25 0.00

28. Models of user engagement – Recap & Next User engagement is complex and standard metrics capture only a part of it User engagement depends on users and time First step towards a taxonomy of models of user engagement … and associated metrics Next Interaction between models Interaction between sites (complex networks) User demographics, time of the day, geo-location, etc

29. Focus – Two “opposite” studies models USER ENGAGEMENT 1. Models of user engagement 2. On saliency, attention and positive effect activity metrics loyalty metrics popularity metrics hobbies navigation social media e-commerce magazine sport news search weather mail weekly news … focused attention affect (emotion) saliency

30. 2. Saliency, attention and positive affect How the visual catchiness ( saliency ) of “relevant” information impacts user engagement metrics such as focused attention and emotion (affect) focused attention refers to the exclusion of other things affect relates to the emotions experienced during the interaction Saliency model of visual attention developed by Itti and Koch L. Itti and C. Koch. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40, 10-12 (2000).

31. Manipulating saliency salient condition non-salient condition Web page screenshot Saliency maps

32. Study design 8 tasks = finding latest news or headline on celebrity or entertainment topic Affect measured pre- and post- task using the Positive e.g. “determined”, “attentive” and Negative e.g. “hostile”, “afraid ” Affect Schedule (PANAS) Focused attention measured with 7-item focused attention subscale e.g. “ I was so involved in my news tasks that I lost track of time”, “ I blocked things out around me when I was completing the news tasks ” and perceived time Interest level in topics (pre-task) and questionnaire (post-task) e.g. “I was interested in the content of the web pages”, “I wanted to find out more about the topics that I encountered on the web pages” 189 (90+99) participants from Amazon Mechanical Turk

33. PANAS (10 positive items and 10 negative items) You feel this way right now, that is, at the present moment [1 = very slightly or not at all; 2 = a little; 3 = moderately; 4 = quite a bit; 5 = extremely] [randomize items] distressed, upset, guilty, scared, hostile, irritable, ashamed, nervous, jittery, afraid interested, excited, strong, enthusiastic, proud, alert, inspired, determined, attentive, active D. Watson, L.A. Clark & A. Tellegen. Development and validation of brief measures of positive and negative affect: The PANAS Scales. Journal of Personality and Social Psychology, 47 (1988).

34. 7-item focused attention subscale (part of the 31-item user engagement scale) 5-point scale (strong disagree to strong agree) I lost myself in this news tasks experience I was so involved in my news tasks that I lost track of time I blocked things out around me when I was completing the news tasks When I was performing these news tasks, I lost track of the world around me The time I spent performing these news tasks just slipped away I was absorbed in my news tasks During the news tasks experience I let myself go H.L. O'Brien. Defining and Measuring Engagement in User Experiences with Technology. PhD Thesis, 2008.

35. Study protocol Mechanical Turk: Introduction/Consent, HIT acceptance Link to external survey; entry of MTurk worker ID Pre-task PANAS Pre-task topic interest questions 8 Non-Salient tasks 8 Salient tasks Self-report of time spent finding headlines Post-task PANAS Self-report of affect Focused attention scale Self-report of focused attention Interest and task ease questions Demographics questionnaire Comments Return to MTurk to submit HIT

36. Saliency and positive affect When headlines are visually non-salient users are slow at finding them, report more distraction due to web page features, and show a drop in affect When headlines are visually catchy or salient user find them faster, report that it is easy to focus, and maintain positive affect Saliency is helpful in task performance, focusing/avoiding distraction and in maintaining positive affect

37. Saliency and focused attention Adapted focused attention subscale from the online shopping domain to entertainment news domain Users reported “easier to focus in the salient condition” BUT no significant improvement in the focused attention subscale or differences in perceived time spent on tasks User interest in web page content is a good predictor of focused attention, which in turn is a good predictor of positive affect

38. Saliency and user engagement – Recap & Next interaction of saliency, focused attention, and affect, together with user interest , is complex Next: include web page content as a quality of user engagement in focused attention scale more “realistic” user (interactive) reading experience bio-metrics (mouse-tracking, eye-tracking, facial expression, etc) L. McCay-Peet, M. Lalmas, V. Navalpakkam. On saliency, affecr and focused attention, CHI 2012

39. The big picture ……….… my vision Page stylistics + layout +links + saliency Measurements and methodologies + online analytics metrics (dwell time, …) + questionnaires + crowd-sourcing - biometrics (eye tracking, mouse tracking, …) Goals + Models of user engagement - Metrics of user engagement Engagement types + attention + emotion + activity + popularity + loyalty - functionality +/- intent & interest user engagement within and across site

40. Gracias [email_address] www.dcs.gla.ac.uk/~mounia