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Framing climate change in Nature and Science editorials: applications of supervised and unsupervised text categorization

Author

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  • Manfred Stede

    (University of Potsdam)

  • Yannic Bracke

    (University of Potsdam
    Berlin-Brandenburg Academy of Sciences and Humanities)

  • Luka Borec

    (University of Potsdam)

  • Neele Charlotte Kinkel

    (University of Potsdam)

  • Maria Skeppstedt

    (Uppsala University)

Abstract

Hulme et al. (Nat Clim Change, 8:515–521, 2018) manually coded ‘frames’ in 490 Nature and Science editorials (1966–2016) they found relevant for climate change. We produced a digital version of the corpus and conducted a set of experiments: We explored many variants of supervised categorization for automatically reproducing the manual frame coding, and we ran an interactive variant of topic modeling. In both approaches, we made use of word embedding techniques for representing text documents. Supervised classification yielded F1-scores of up to 0.91 (for the best category) and 0.68 overall, and it led to insights regarding the relation between ‘topic’ and ‘framing’. The topic modeling algorithm was able to reproduce central trends in the temporal analysis of framing that was presented by Hulme et al. based on their manual work.

Suggested Citation

  • Manfred Stede & Yannic Bracke & Luka Borec & Neele Charlotte Kinkel & Maria Skeppstedt, 2023. "Framing climate change in Nature and Science editorials: applications of supervised and unsupervised text categorization," Journal of Computational Social Science, Springer, vol. 6(2), pages 485-513, October.
    • Handle: RePEc:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-023-00199-7
    DOI: 10.1007/s42001-023-00199-7
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    References listed on IDEAS

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    1. Eric P. S. Baumer & David Mimno & Shion Guha & Emily Quan & Geri K. Gay, 2017. "Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1397-1410, June.
    2. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    3. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
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