Web Structure Derived Clustering for Optimised Web Accessibility Evaluation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Web accessibility evaluation is a costly and complex process due to limited time, resources and ambiguity. To optimise the accessibility evaluation process, we aim to reduce the number of pages auditors must review by employing statistically representative pages, reducing a site of thousands of pages to a manageable review of archetypal pages. Our paper focuses on representativeness, one of six proposed metrics that form our methodology, to address the limitations we have identified with the W3C Website Accessibility
Conformance Evaluation Methodology (WCAG-EM). These include the evaluative scope, the non-probabilistic sampling approach, and the potential for bias within the selected sample. Representativeness, in particular, is a metric to assess the quality and coverage of sampling. To measure this, we systematically evaluate five web page representations with a website of 388 pages, including tags, structure, the DOM tree, content, and a mixture of structure and content. Our findings highlight the importance of including structural components in representations. We validate our conclusions using the same methodology for three additional random sites of 500 pages. As an exclusive attribute, we find that features derived from web content are suboptimal and can lead to lower quality and more disparate clustering for optimised accessibility evaluation.
Original languageEnglish
Title of host publicationInternational World Wide Web Conference 2023
Number of pages10
ISBN (Electronic)9781450394161
Publication statusPublished - 30 Apr 2023
EventInternational World Wide Web Conference 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023

Publication series

NameACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023


ConferenceInternational World Wide Web Conference 2023
Country/TerritoryUnited States


  • Accessibility
  • Automated evaluation
  • Clustering
  • Sampling


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