Predicting whether users view dynamic content on the World Wide Web (and beyond...)
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This is the presentation given at CHI 2014 for the paper: 'Predicting whether users view dynamic content on the world wide web (Caroline Jay, Andy Brown, Simon Harper), In ACM Trans. Comput.-Hum. Interact., ACM, volume 20, 2013.' http://doi.acm.org/10.1145/2463579.2463580 It describes a generalizable technique and model for predicting whether people view content such as tickers, slideshows and suggestion lists on the Web, and discusses how this work is now moving into the TV domain. See the presentation in action on YouTube: https://www.youtube.com/watch?v=NvS8cRXCvz8
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Predicting whether users view dynamic content on the World Wide Web (and beyond...)
- 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
- 9. All other updates 2.8-6.2 20% >6.2 30% <2.8 6% >2.8 81% 1.2-2.8 59% 0.6-1.2 41% <0.6 16% None 20% Duration (s) Duration (s) Keystroke/Enter/Hover 41% 9
- 10. Why does the model take this form? • Area (and action) are properties of the update. – As an update increases in size it becomes more salient. • Duration is sometimes a property of the update, and sometimes a property of user behaviour. – The longer a suggestion list appears on the screen, the more likely it is to be viewed. – People pause to view the content. 10
- 11. Translating dynamic updates to audio • FireFox plugin – Prioritize click-activated updates. – Deliver keystroke-activated updates whenever there is a pause in typing. – Opt-in to receiving automatic updates. • Preferred by all participants in blind and double-blind evaluation when compared with FireVox baseline. 11
- 12. A conversation with BBC R&D • Can we predict behaviour with other types of media? • Can we use this to drive future media development? 12
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- 17. Eye tracking TV viewing C. Jay, A. Brown, M. Glancy, M. Armstrong, S. Harper (2013). Attention approximation: from the Web to multi-screen television. TVUX- 2013@CHI. http://goo.gl/dvAp3V Brown, M. Evans, C. Jay, M. Glancy, R. Jones, S. Harper (2014). HCI over multiple screens. CHI EA: alt.chi 2014. http://goo.gl/UJhPC5 17
- 19. Participant and context characteristics 19
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- 21. Driving future media development with empirical models • It’s complicated – Ecological validity – Influence vs. inference • Model according to application – Production design software • Ultimate aim – To develop a compelling media experience – To advance craft-based engineering with science 21
- 22. Find out more Publications, reports and data: http://goo.gl/1h4z4K caroline.jay@manchester.ac.uk The Web Ergonomics Lab The University of Manchester, UK http://wel.cs.manchester.ac.uk/ 22
- 23. A future media interaction model • Dynamic updates – Requested (clicked) updates very likely to be viewed. – Automatic updates also likely to be viewed. • Audio • Context • Participant characteristics 23
Notas del editor
- The starting point for this presentation is a TOCHI paper, which describes a simple and accurate model that tells us how likely it is that people will view dynamic web content. By dynamic web content we mean things like suggestion lists, slide shows, tickers hover over menus, animations, videos – anything that changes on a web page whilst the URL stays the same.
- Some previous work in this area Some controlled studies that movement had an effect, others saying it didn’t.
- We realised pretty early on that a controlled that designing a controlled study at the outset was not going to be possible. There are a plethora of dynamic updates – we had no idea what a typical update looked like – and we wanted to be able to deal with any of them.
- Virtually all modelling based on predicting or understanding performance as a function of task. Study just talked about as it was only loosely based on task (trying to find errors in spreadsheet). In real world, never going to know person’s task. Not to say we ignore task – may be able to infer what’s happening, and use that to help us understand behaviour – but that it’s not possible to know before somebody has done something, what they are going to do. Especially true of complex web apps. Still useful to be able to predict behaviour though – not least because knowing how someone will respond to the perceptual characteristics of UI components could help with design.
- 1486 updates 585 validators
- Not surprising -
- Can the model tell us anything about how people view television content? (picture of final score, red button, dual screen)
- Can the model tell us anything about how people view television content? (picture of final score, red button, dual screen)
- Can the model tell us anything about how people view television content? (picture of final score, red button, dual screen)
- Watching TV now involves more than one screen. People have been using second screens, mobile devices such as tablets or phones, for a while, but much of this has been viewer-led – e.g. looking up additional info or social media. Broadcasters are really keen to exploit this, so they are starting to do develop companion content for what’s happening on main screen. Can see Secret Fortune – play at home. Broadcasters want to go way beyond this, but at the moment this type of interaction is not well understood. Has been SS research, but mostly looking at social aspects of SS use. What we don’t have are models describing cognitive and perceptual aspects of multiple device interaction: how do people split their attention between devices? What are the factors that influence attention orientation? This is what we’re trying to investigate with this work.
- So far we’ve run eye tracking studies examining two scenarios: additional content on the TV, and additional content on a companion device. The methods for this work have been described in CHI presentations last year and this year, so I won’t go into detail about how they were run, but I will share some of the more interesting results with you.
- This is a graph from the study looking at how people split attention across a TV and a companion device and a tablet, while they are watching a nature documentary with a companion app. The top half of the figure shows the percentage of participants looking at the TV in a given 5 second period, and the bottom half shows the percentage of people looking at the tablet. The think black lines indicate when content has updated on the tablet, and we can see that this is something that attracts attention – participants are seeing the change in their peripheral vision, and looking down to see what’s happened.
- Explored the methodological issues – haven’t explored what the data actually means. Will do this tomorrow.
- We can start to see what