W4A 2010 - Web Not For All: A Large Scale Study of Web Accessibility

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1. Web Not For All A Large Scale Study of Web Accessibility Rui Lopes1, Daniel Gomes2, Luís Carriço1 1 LaSIGE, University of Lisbon 2 FCCN 2. Context ã The Web is the…
  • 1. Web Not For All A Large Scale Study of Web Accessibility Rui Lopes1, Daniel Gomes2, Luís Carriço1 1 LaSIGE, University of Lisbon 2 FCCN
  • 2. Context • The Web is the biggest information source for Mankind. Decentralised architecture made it blossom. • Humans (and computers!) contribute to information production and consumption, leading to ~45B Web pages.
  • 3. Context • Growth of users contributing and interacting with the Web leads to significant diversity of users, including people with disabilities. • The openness and decentralisation of the Web leads to an uncontrolled quality check of Websites’ usability (and accessibility).
  • 4. What is the state of accessibility on the Web?
  • 5. • It is known that Web accessibility adequacy is often far worse than desired. • Studies tend to focus on a restricted (small) set of Web sites. • Do macroscopic properties of Web accessibility emerge from analysing at a large scale?
  • 6. Experiment background • The Portuguese Web Archive initiative periodically crawls contents from the Portuguese Web (.pt and others) for future preservation. • Services are built on top of crawled collections: search (end users) & analysis framework (researchers).
  • 7. Methodology data acquisition - obtaining the document collection • Collect a sufficiently large portion of the Web, yet representative (e.g., national Webs) • Spider traps handled gracefully • Boostraped with 200,000 Website addresses from the .pt TLD • Collected March/May 2008
  • 8. Methodology data acquisition - evaluation process • Implementation of 39 WCAG 1.0 checkpoints yield pass, fail, warn. (collection previous to WCAG 2.0 TR) • Overcome computational effort with Hadoop cluster, streams, caching, etc.
  • 9. Methodology data analysis • Failure rate, 3 criteria:
  • 10. Results general • 28M Web pages were evaluated. (58%) • 21GB evaluation data collected for analysis. • 40B HTML elements evaluated. (~1500/page) • 1.5B elements passed. (56/page, 3.89%) • 2.9B elements failed. (103/page, 7.15%) • 36B elements warned. (1291/page, 89%)
  • 11. Results rates versus page count distribution conservative optimistic strict
  • 12. Results rates versus page complexity (# HTML elements) conservative optimistic strict
  • 13. Discussion on the results • Large scale confirms predictions of small scale studies - the Web is still not for all. • Smaller Web pages tend to have greater accessibility quality. • Nature of warnings is more striking than expected, completely different interpretations. • Automated evaluation is just the beginning.
  • 14. Discussion on the limitations of the experiment • HTML structure vs. content rhetorics. (CSS & Javascript can change it all) • Collecting the Web is hard. (deep Web - AJAX & forms -, infinite generation, robots.txt, etc.) • Scaling evaluation & analysis processes is hard. (evaluation streamability, resource inter-dependencies, billion node graphs, etc.)
  • 15. Conclusions • Large scale accessibility evaluation of the Portuguese Web. • Re-confirmed studies at the large. • Educating developers & designers about warnings is crucial for accessibility success! • Automated evaluation is just the start. Always need for expert & users evaluations.
  • 16. Ongoing Work we’re still at the tip of the iceberg • Linking properties (ranking vs. accessibility) • Evolution of accessibility compliance in time (different document collections) • Cross-cuts: gov, e-com, personalisation, etc. • Developing countries countries) (Portuguese speaking African
  • 17. Ongoing Work help wanted from community! • Making available evaluation datasets (e.g., Linked Data). Ours and yours! • Larger document collections. • Transforming warnings into failures with machine learning.
  • 18. Thank you! rlopes@di.fc.ul.pt
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