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Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities

Author

Listed:
  • Albert Weichselbraun

    (Institute for Information Research, University of Applied Sciences of the Grisons, 7000 Chur, Switzerland)

  • Philipp Kuntschik

    (Institute for Information Research, University of Applied Sciences of the Grisons, 7000 Chur, Switzerland)

  • Vincenzo Francolino

    (Institute for Information Research, University of Applied Sciences of the Grisons, 7000 Chur, Switzerland)

  • Mirco Saner

    (IAM Institute of Applied Media Studies, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland)

  • Urs Dahinden

    (Institute for Information Research, University of Applied Sciences of the Grisons, 7000 Chur, Switzerland)

  • Vinzenz Wyss

    (IAM Institute of Applied Media Studies, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland)

Abstract

Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.

Suggested Citation

  • Albert Weichselbraun & Philipp Kuntschik & Vincenzo Francolino & Mirco Saner & Urs Dahinden & Vinzenz Wyss, 2021. "Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities," Future Internet, MDPI, vol. 13(3), pages 1-22, February.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:59-:d:506442
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    References listed on IDEAS

    as
    1. Wingyan Chung & Daniel Zeng, 2016. "Social-media-based public policy informatics: Sentiment and network analyses of U.S. Immigration and border security," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(7), pages 1588-1606, July.
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    Cited by:

    1. Filipe Portela, 2021. "Data Science and Knowledge Discovery," Future Internet, MDPI, vol. 13(7), pages 1-4, July.
    2. Bernhard Standl & Nadine Schlomske-Bodenstein, 2021. "A Pattern Mining Method for Teaching Practices," Future Internet, MDPI, vol. 13(5), pages 1-14, April.

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