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dc.contributor.authorLiu, Shali
dc.date.accessioned2016-01-05T15:52:19Z
dc.date.available2016-01-05T15:52:19Z
dc.identifier.urihttp://hdl.handle.net/10222/65223
dc.description.abstractOur research focuses on evaluating the efficiency of the combination of the computed scores and visual representations of features. W conducted a two-phased user study. The first phase were designed to determine the important text features. In the first phase, participants were asked to rate a set of Twitter data and prominent features. After the key feature set was determined, we started to evaluate the two sources of results to be visualized: 1) informativeness and relatedness scores and 2) visual features. We built four versions of visualizations to represent the full projection of the two sources. In the second phase of the user study, 48 participants were recruited to do a between-subject user study for evaluating the four versions of visualizations using the same set of information seeking tasks. The study shows that the visualization combining scores and features perform the best in efficiency for browsing tasks.en_US
dc.language.isoenen_US
dc.subjectHCIen_US
dc.subjectVisual Analyticsen_US
dc.subjectTwitteren_US
dc.titleEvaluating the Effectiveness of Visualization Design for Twitter Conversations on Academic Topicsen_US
dc.typeThesis
dc.date.defence2015-12-15
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDr. Norbert Zehen_US
dc.contributor.thesis-readerDr. Derek Reillyen_US
dc.contributor.thesis-readerDr. Bonnie Mackayen_US
dc.contributor.thesis-supervisorDr. Evangelos Miliosen_US
dc.contributor.ethics-approvalReceiveden_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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