Learning from climate change news: Is the world on the same page?

Like the classification models, the regression model mostly produced features unrelated to climate change. These are much like those yielded in classification, but were not analyzed in- depth. Of the 250 positive and negative features extracted, 6 to 12% were analyzed as relevant to climate change (Earlier: 16, Later: 29). The full list of features is available, as described under Reproducibility. Discussion & conclusions This study has uncovered relevant topical and terminological differences between newspaper articles on climate change in Australia, Canada, and the United Kingdom. In doing so, it has made use of supervised machine learning. Since most of the studies on climate change news focus on single countries and make use of manual content analysis [17], we believe that our research can positively contribute to the development of the field. The study aimed to find a response to three research questions: Are there major topical and terminological differences between the articles on climate change by newspapers from differ- ent countries (RQ1)?; Are there major topical and terminological differences between the arti- cles on climate change by newspapers with different political orientations (RQ2)?; Are there major topical and terminological differences between the newspaper articles on climate change during different COP meetings (RQ3)? The results of our machine learning model reveal that countries can be automatically classi- fied accurately, with a macro F1-score of 0.90. Based on these findings and the analysis of most

informative features, it can also be concluded that there are topical and terminological differ- ences between these countries (RQ1). These differences have to do with climate change termi- nology, and its effects, causes, and measures. The model for political orientations had the second best fit, scoring a macro F1-score of 0.80. This result allows us to conclude that it is possible to automatically classify articles into the political orientation of their newspaper. This means that distinctive features (topical and terminological) for political orientation also exist in the corpus (RQ2). These differences, in line with the findings reached in the country model, are terminological and have to do with the causes, measures, and effects of climate change news articles. The results of the models for differentiating between COP meetings are slightly less outspo- ken. It is not possible to convincingly distinguish every COP edition. Indeed, the macro F1 score was 0.43, although it greatly differed between the meetings. This signals that not every meeting was equally relevant. However, it can be concluded that the model detected certain differences, seeing that the baseline was outperformed. When assessed as a regression task, taking into account the temporal nature of these labels, an RMSE of 4.07 was yielded. Thus, the COP meeting can properly be estimated up to roughly half a decade. Regression shows to be a more suitable method to predict the COP meeting for newspaper articles. This leads us to conclude that it is possible to automatically predict the

COP meeting sufficiently accurately. Some COP meetings do indeed show relevant topical and terminological differences, while others hardly do (RQ3). Those that do feature these differences show a clear terminology tran- sition over the years and differences in conference-related phrases, as well as effects, causes, and measures. It also becomes clear that later COPs show more relevant features and more urgent phrases than earlier ones. Specifically, terminology has shifted to become more descrip- tive of a trend (greenhouse vs. climate) and more extreme (climate crisis); describing causes is something of the later editions (e.g., fracking, drilling, oilsands); and measures are becoming more and more subject of attention (e.g., electric vehicles, renewables, emissions reductions). PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 22 / 27