1. Predicting whether users view dynamic content on the World Wide Web Caroline Jay, Andy Brown, Simon Harper Web Ergonomics Lab, University of Manchester 1 1 Presenter: Caroline Jay caroline.jay@manchester.ac.uk 2. Driving future media development with empirical models Future media = the future of entertainment Web content, television, apps, other stuff Can we create conceptual representations of interaction based entirely on data, that we can use to develop future media provision? Yes. (Probably.) 2 3. Challenge Model must predict future observations. Internal validity: reliably predicts observations in the same setting. External validity: reliably predicts observations in other settings. 3 What is the appropriate paradigm for building this type of model? 4. Translating Web content to audio Screen readers handled dynamic updates badly. If we understood how sighted users view updates, could we translate them to audio more effectively? 4 SASWAT project, funded by EPSRC (EP/E062954/1) 5. Exploratory study Participants completed tasks on sites that contained dynamic content. No constraints on how task was completed. No constraints on where task was completed. Nine minutes of browsing. 5 6. Data-driven analysis Can we predict whether people view dynamic updates as a function of their characteristics? Chi-squared Interaction Detector (CHAID) analysis Action: click, hover, keystroke, enter, none Area: cm2 Duration: seconds (participant) (addition or replacement) Validation data from later study 6 7. Results CHAID model predicts viewing behaviour with an accuracy of ~80% Best predictor: action Keystroke/Enter/Hover 41% None 20% Click 77% Action 7 8. 1.1-7.8 71% 7.8-32.9 90% >32.9 99% 6.2 30% 2.8 81% 1.2-2.8 59% 0.6-1.2 41%