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Visualization Needs in Computational Social Sciences

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2019

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ACM

Zusammenfassung

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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Heuer, Hendrik; Polizzotto, Anna; Marx, Franziska; Breiter, Andreas (2019): Visualization Needs in Computational Social Sciences. Mensch und Computer 2019 - Tagungsband. DOI: 10.1145/3340764.3344440. New York: ACM. MCI: Short Paper (Poster). Hamburg. 8.-11. September 2019

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