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

UK: low carbon AU: uranium CA/AU: carbon tax Apart from these climate change-related features, the majority of most informative features were seemingly unrelated to climate change. Yet, they were used by the model to distinguish countries. Only a handful of features were analyzed (UK: 7, CA: 6, AU: 11), while 200 most informative features were extracted (100 positive and 100 negative) per country. Percentually, that means only 3% to 6% of features were found directly relevant to climate change. Most of these non-climate change features can be described as ortographical differences (e.g. program vs. programme), lexical variations (e.g. transport vs. transportation), local currencies (e.g. pounds vs. dollar), political terminology (e.g. treasurer vs. chancellor), national sports (e.g. foot- ball vs. soccer), and geographical and demographical terminology (e.g. province vs. state). No feasible explanation could be found for plenty of other features. The full list of extracted fea- tures is accessible as instructed under Reproducibility. In sum, our results, in line with prior research in the field, point to important differences among the countries with regard to the effects, causes, and solutions to climate change. For example, while for Australia “mining” is shown to be a distinctive cause of climate change, in the case of UK this is “flights” and for Canada “oil sands” comes up. These features seem to respond to specific economic and political contexts [34] of the countries: London serves as the

largest aviation hub in the world by passenger traffic (en.wikipedia.org/wiki/List_of_busiest_ airports_in_the_United_Kingdom), Australia is the world’s largest producer of iron ore and bauxite and the second largest of gold (en.wikipedia.org/wiki/Mining_in_Australia), and Alberta’s (Canada) oils sands, is the world’s third-largest proven oil reserve at 170 billion bar- rels (nationalgeographic.com/environment/article/alberta-canadas-tar-sands-is-growing-but- indigenous-people-fight-back). Political orientations For the political orientations model, the average F1-score was 0.80. This was somewhat lower than for the countries but still meant the vast majority of articles could correctly be classified. Hence, analysis of the most informative features was also sensible for this model. It showed the same picture as for the countries: the model relies on many features that indi- cate differences irrelevant to climate change. Moreover, the model also relies on features that were earlier found to be indicative of individual countries. This is surprising, for every country is represented on both sides of the political spectrum. However, some interesting observations can be made both literally and figuratively between the lines of these less relevant features. These observations can help answer the second research question (“What major topical and termino- logical differences can be found between political orientations and how could they be explained?”). When it comes to climate change-related features, the right and left show several contrasts.

In general, it turns out easier to find related terminology that is indicative of left-oriented newspapers. This could suggest that these also cover the subject more frequently. It also raises the question of what the model picked up instead. The share of features that seem to be country indicators or political themes is especially larger for right-oriented articles. Apart from those phrases, the features seem highly irrelevant. The relevant keywords can be characterized using a similar structure as for the model for countries: • Terminology The right and the left side of politics seem to use different words for the matter of climate PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 16 / 27