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  • × author_ss:"Gill, A.J."
  • × theme_ss:"Computerlinguistik"
  1. Vasalou, A.; Gill, A.J.; Mazanderani, F.; Papoutsi, C.; Joinson, A.: Privacy dictionary : a new resource for the automated content analysis of privacy (2011) 0.00 
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    Abstract
    This article presents the privacy dictionary, a new linguistic resource for automated content analysis on privacy-related texts. To overcome the definitional challenges inherent in privacy research, the dictionary was informed by an inclusive set of relevant theoretical perspectives. Using methods from corpus linguistics, we constructed and validated eight dictionary categories on empirical material from a wide range of privacy-sensitive contexts. It was shown that the dictionary categories are able to measure unique linguistic patterns within privacy discussions. At a time when privacy considerations are increasing and online resources provide ever-growing quantities of textual data, the privacy dictionary can play a significant role not only for research in the social sciences but also in technology design and policymaking.
    Type
    a
  2. Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017) 0.00 
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    Abstract
    Analyzing large quantities of real-world textual data has the potential to provide new insights for researchers. However, such data present challenges for both human and computational methods, requiring a diverse range of specialist skills, often shared across a number of individuals. In this paper we use the analysis of a real-world data set as our case study, and use this exploration as a demonstration of our "insight workflow," which we present for use and adaptation by other researchers. The data we use are impact case study documents collected as part of the UK Research Excellence Framework (REF), consisting of 6,679 documents and 6.25 million words; the analysis was commissioned by the Higher Education Funding Council for England (published as report HEFCE 2015). In our exploration and analysis we used a variety of techniques, ranging from keyword in context and frequency information to more sophisticated methods (topic modeling), with these automated techniques providing an empirical point of entry for in-depth and intensive human analysis. We present the 60 topics to demonstrate the output of our methods, and illustrate how the variety of analysis techniques can be combined to provide insights. We note potential limitations and propose future work.
    Type
    a

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