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    Extending Open Data Platforms with Storytelling Features


    Brolcháin, Niall Ó, Porwol, Lukasz, Ojo, Adegboyega, Wagner, Tilman, Lopez, Eva Tamara and Karstens, Eric (2017) Extending Open Data Platforms with Storytelling Features. In: Proceedings of the 18th Annual International Conference on Digital Government Research. ACM Digital Library, pp. 48-53. ISBN 978-1-4503-5317-5

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    Abstract

    Research into Data-Driven Storytelling using Open Data has led to considerable discussion into many possible futures for storytelling and journalism in a Data-Driven world, in particular, into the Open Data directives framed by various governments across the globe as a means of facilitating governments, transparency enabled citizens and journalists to get more insights into government actions and enable deeper and easier monitoring of governments’ work. While progress in the development of Open Data platforms (usually funded by national and local governments) has been significant, it is only now that we are beginning to see the emergence of more practical and more applied use of Open Data platforms. Previous works have highlighted the potential for storytelling using Open Data as a source of information for journalistic stories. Nevertheless, there is a paucity of studies into Open Data platform affordances to support Data-Driven Storytelling. In this paper, we elaborate on existing Open Data platforms in terms of support for storytelling and analyse feedback from stakeholder focus groups, to discover what methods and tools can introduce or facilitate the storytelling capabilities of Open Data platforms.
    Item Type: Book Section
    Keywords: Usable Open Data Platform; Data-Driven Storytelling; Data-Driven Journalism; Journalism; YDS Platform; Open Data;
    Academic Unit: Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI
    Faculty of Social Sciences > School of Business
    Item ID: 15810
    Identification Number: 10.1145/3085228.3085283
    Depositing User: Adegboyega Ojo
    Date Deposited: 12 Apr 2022 13:35
    Publisher: ACM Digital Library
    Refereed: Yes
    Related URLs:
    URI: https://mu.eprints-hosting.org/id/eprint/15810
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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