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

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.