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

change. The right just seems to name it by the climate change. The left is more outspoken, by referring to a crisis. Furthermore, they use greenhouse and environment more. Seeing that features that were indicative of countries came back in this model, it could however be that these insights were also subject to this effect. L: greenhouse, environment, crisis R: the climate change • Causes The model also picked up on the causes of climate change. The left refers to polluters, who might in their perspective have to pay. This relates to the phrases transport and fossil fuel. The right side only seems to consider aircraft a worthy cause of the changing climate. L: polluters, transport, fossil fuel R: aircraft • Measures When it comes to measures against climate change, the right is again overtaken by the left. Only carbon tax was found for right-oriented articles. This is rather surprising, but could well mean that the right articles argue against it. Liberal journalism speaks about energy effi- ciency, climate talks and cooperation, like during COPs. Lastly, sustainable initiatives also seem to be on their agenda. L: energy efficiency, climate talks, cooperation, sustainable R: carbon tax • Effects The model did not value many features related to the effects of climate change. Only forests as found indicative for left-oriented newspapers. L: forests Again, apart from the features analyzed (L: 11, R: 3), the majority of the 100 most informa- tive features extracted for each orientation were unrelated to climate change. The share of rele-

vant features is 3% for right-oriented newspapers and 11% for left-oriented ones. Several non- climate related features that were earlier observed as informative for countries, were also infor- mative for the political orientations. Examples are local currencies, lexical variations and politi- cal differences. Moreover, specific political themes come up, that are typically favored by specific sides of the political spectrum (e.g. L: housing, schools; R: business, market). Please refer to Reproducibility, for access to the full list of extracted features. In a nutshell, our findings reveal a clear unbalance between the features of left-oriented newspapers (11) and those of the right-oriented newspapers (4). Furthermore, our results point to different causes (e.g. L: fossil fuel; R: aircraft) and measures (e.g. L: energy efficiency; R: carbon tax) between the newspapers. Thus, in line with findings by Chinn et al. [11] and Pot- thast et al. [10], our research also reveals differences due to political orientations. COP meetings The model for COP meetings scored a much lower macro average F1-score of 0.43. This inevi- tably means that the model has learned features that were not effective in distinguishing the labels in the Test data. The average F1-score comes about from a variety of F1-scores per label. Some meetings could hardly be detected (COP10, 0.18), while others still scored a fair performance (COP25, 0.62). It was considered that all labels scoring below 0.50 on F1 were too unreliable to be ana-

lyzed. Only 8 labels scored above this threshold, being COP4, 15, and 20 until 25. This list was PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 17 / 27