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  • 标题:Computational Impact Assessment of Social Justice Documentaries
  • 本地全文:下载
  • 作者:Jana Diesner ; Jinseok Kim ; Susie Pak
  • 期刊名称:Journal of Electronic Publishing
  • 印刷版ISSN:1080-2711
  • 电子版ISSN:1080-2711
  • 出版年度:2014
  • 卷号:17
  • 期号:3
  • 页码:1-15
  • DOI:10.3998/3336451.0017.306
  • 出版社:University of Michigan Press
  • 摘要:Documentaries are meant to tell a story, that is, to create memory, spark the imagination and support sharing (Rose, 2012). Moreover, documentaries aim to change people’s knowledge and/ or behavior (Barrett & Leddy, 2008). How can we know if a documentary has achieved these goals? We report on a research project where we have been developing, applying and evaluating a theoretically–grounded, empirical and computational solution for assessing the impact of social justice documentaries in a scalable, robust and rigorous fashion. We leverage cutting–edge methods from socio– technical data analytics—namely natural language processing and network analysis—for this purpose. We also built a publicly available software tool (ConText) that supports these routines. In this paper, we focus on the theoretical framework for this project, present our methodology, and provide an illustrative example of the proposed solution.
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