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  Text as big data: Develop codes of practice for rigorous computational text analysis in energy social science

Müller-Hansen, F., Callaghan, M. W., Minx, J. C. (2020): Text as big data: Develop codes of practice for rigorous computational text analysis in energy social science. - Energy Research and Social Science, 70, 101691.
https://doi.org/10.1016/j.erss.2020.101691

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_25217 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_25217_4
Genre: Journal Article

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 Creators:
Müller-Hansen, Finn1, Author              
Callaghan, Max W.2, Author
Minx, Jan C.2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Augmenting traditional social science methods with computational analysis is crucial if we are to exploit the vast digital archives of text data that have become available over the past two decades. In this journal, Benites-Lazaro et al. [1] showcase this in an application of topic modeling and other computational methods to an actor-specific examination of changes in policy discourse on ethanol in Brazil and point out methodological promises and challenges. However, their contribution also highlights the need for establishing codes of practice for computational text analysis. In this perspective, we discuss five areas for improvement when treating text as big data in light of guiding principles from computational research – transparency, reproducibility and validation – to facilitate rigorous research practice: (1) full transparency over data collection and corpus construction, (2) comprehensive method descriptions that enable reproducibility by other researchers, (3) application of rigorous model validation procedures, (4) results interpretation based on primary text and clear research design and (5) critical discussion and contextualization of main findings. We conclude that the energy social science community needs to develop codes of practice to build on the promising research within the field of computational text analysis and suggest first steps into this direction.

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 Dates: 2020-12-01
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.erss.2020.101691
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
PIKDOMAIN: RD4 - Complexity Science
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Title: Energy Research and Social Science
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 70 Sequence Number: 101691 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/energy-research-social-science
Publisher: Elsevier