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Open-Source’s Inspirations for Computational Social Science: Lessons from a Failed Analysis

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  • Nathaniel Poor

    (Underwood Institute, USA)

Abstract

The questions we can ask currently, building on decades of research, call for advanced methods and understanding. We now have large, complex data sets that require more than complex statistical analysis to yield human answers. Yet as some researchers have pointed out, we also have challenges, especially in computational social science. In a recent project I faced several such challenges and eventually realized that the relevant issues were familiar to users of free and open-source software. I needed a team with diverse skills and knowledge to tackle methods, theories, and topics. We needed to iterate over the entire project: from the initial theories to the data to the methods to the results. We had to understand how to work when some data was freely available but other data that might benefit the research was not. More broadly, computational social scientists may need creative solutions to slippery problems, such as restrictions imposed by terms of service for sites from which we wish to gather data. Are these terms legal, are they enforced, or do our institutional review boards care? Lastly—perhaps most importantly and dauntingly—we may need to challenge laws relating to digital data and access, although so far this conflict has been rare. Can we succeed as open-source advocates have?

Suggested Citation

  • Nathaniel Poor, 2020. "Open-Source’s Inspirations for Computational Social Science: Lessons from a Failed Analysis," Media and Communication, Cogitatio Press, vol. 8(3), pages 231-238.
    • Handle: RePEc:cog:meanco:v:8:y:2020:i:3:p:231-238
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    References listed on IDEAS

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    1. Johannes Breuer & Tim Wulf & M. Rohangis Mohseni, 2020. "New Formats, New Methods: Computational Approaches as a Way Forward for Media Entertainment Research," Media and Communication, Cogitatio Press, vol. 8(3), pages 147-152.

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