DATA-DRIVEN STORYTELLING FOR CASUAL USERS

dc.contributor.advisorElmqvist, Niklasen_US
dc.contributor.authorZhao, Zhenpengen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2019-09-27T05:34:52Z
dc.date.available2019-09-27T05:34:52Z
dc.date.issued2019en_US
dc.description.abstractToday’s overwhelming volume of data has made effective analysis virtually inaccessible for the general public. The emerging practice of data-driven storytelling is addressing this by framing data using familiar mechanisms such as slideshows, videos, and comics to make even highly complex phenomena understandable. However, current data stories still do not utilize the full potential of the storytelling domain. One reason for this is that current data-driven storytelling practice does not leverage the full repertoire of media that can be used for storytelling, such as speech, e-learning, and video games. In this dissertation, we propose a taxonomy focused specifically on media types for the purpose of widening the purview of data-driven storytelling by putting more tools in the hands of designers. We expand the idea of data-driven storytelling into the group of casual users, who are the consumers of information and non-professionals with limited time, skills, and motivation , to bridge the data gap between the advanced data analytics tools and everyday internet users. To prove the effectiveness and the wide acceptance of our taxonomy and data-driven storytelling among the casual users, we have collected examples for data-driven storytelling by finding, reviewing, and classifying ninety-one examples. Using our taxonomy as a generative tool, we also explored two novel storytelling mechanisms, including live-streaming analytics videos—DataTV—and sequential art (comics) that dynamically incorporates visual representations—Data Comics. Meanwhile, we widened the genres we explored to fill the gaps in the literature. We also evaluated Data Comics and DataTV with user studies and expert reviews. The results show that Data Comics facilitates data-driven storytelling in terms of inviting reading, aiding memory, and viewing as a story. The results also show that an integrated system as DataTV encourages authors to create and present data stories.en_US
dc.identifierhttps://doi.org/10.13016/auae-uabb
dc.identifier.urihttp://hdl.handle.net/1903/24997
dc.language.isoenen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledDocument comprehensionen_US
dc.subject.pquncontrolledHuman computer interactionen_US
dc.subject.pquncontrolledInformation visualizationen_US
dc.subject.pquncontrolledStorytellingen_US
dc.subject.pquncontrolledTaxonomyen_US
dc.titleDATA-DRIVEN STORYTELLING FOR CASUAL USERSen_US
dc.typeDissertationen_US

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