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dc.contributor.authorSong, S
dc.contributor.authorBu, J
dc.contributor.authorArtmeier, A
dc.contributor.authorShi, K
dc.contributor.authorWang, Y
dc.contributor.authorYu, Z
dc.contributor.authorWang, C
dc.date.accessioned2021-02-01T05:31:43Z
dc.date.available2021-02-01T05:31:43Z
dc.date.issued2018
dc.identifier.issn2573-0142
dc.identifier.doi10.1145/3274432
dc.identifier.urihttp://hdl.handle.net/10072/401612
dc.description.abstractWeb accessibility evaluation examines how well websites comply with accessibility guidelines which help people with disabilities to perceive, navigate and contribute to the Web. This demanding task usually requires manual assessment by experts with many years of training and experience. However, not enough experts are available to carry out the increasing number of evaluation projects while non-experts often have different opinions about the presence of accessibility barriers. Addressing these issues, we introduce a crowdsourcing system with a novel truth inference algorithm to derive reliable and accurate assessments from conflicting opinions of evaluators. Extensive evaluation on 23,901 complex tasks assessed by 50 people with and without disabilities shows that our approach outperforms state of the art approaches. In addition, we conducted surveys to identify frequent barriers that people with disabilities are facing in their daily lives and the difficulty to access Web pages when they encounter these barriers. The frequencies and severities of barriers correlate with their derived importance in our evaluation project.
dc.description.peerreviewedYes
dc.languageen
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofpagefrom163
dc.relation.ispartofissueCSCW
dc.relation.ispartofjournalProceedings of the ACM on Human-Computer Interaction
dc.relation.ispartofvolume2
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode08
dc.titleCrowdsourcing-based web accessibility evaluation with golden maximum likelihood inference
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSong, S; Bu, J; Artmeier, A; Shi, K; Wang, Y; Yu, Z; Wang, C, Crowdsourcing-based web accessibility evaluation with golden maximum likelihood inference, Proceedings of the ACM on Human-Computer Interaction, 2018, 2 (CSCW), pp. 163
dc.date.updated2021-02-01T05:29:01Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Can


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