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Visualization Needs in Computational Social Sciences

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Published:08 September 2019Publication History

ABSTRACT

With the advent of digital humanities and computational social sciences, machine learning techniques like topic modeling are increasingly employed by social scientists and humanities scholars. This poses the question what visualization needs these researchers have when confronted with such complex systems. In this paper, we investigate visualization needs in the context of the topic modeling algorithm Latent Dirichlet Allocation and the 950,000 articles of the New York Times corpus. We presented visualizations of how the topics in the newspaper changed over time to seven participants, who fulfilled three tasks with three visualization types. Qualitative interviews with the participants supported our assumptions that visualizations for these tasks need to be visually appealing, intuitively interpretable, and minimizing mental effort.

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        cover image ACM Other conferences
        MuC '19: Proceedings of Mensch und Computer 2019
        September 2019
        863 pages
        ISBN:9781450371988
        DOI:10.1145/3340764

        Copyright © 2019 ACM

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        • Published: 8 September 2019

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