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

Climate Change (UNFCCC) (unfccc.int/process/bodies/supreme-bodies/conference-of-the- parties-cop). The convention strives to prevent “dangerous” human interference with the cli- mate system (unfccc.int/process-and-meetings/the-convention/what-is-the-united-nations- framework-convention-on-climate-change) and the meetings are the chief platform for key decision-making. These have occurred 26 times between 1995 and 2019. This study approaches climate change from a linguistic perspective. Language plays an important role in tackling the numerous effects of climate change. It facilitates precarious problem-solving during global political summits, it is used in scientific papers that must con- vince politicians to act, and it spreads the news to the general public [1–7]. Precisely the last context is considered crucial in this study. After all, behavioral change by every citizen is key to reducing greenhouse gas emissions. Politics and businesses must play a key role in facilitating this, but for citizens to really change their comings and goings, intrinsic conviction and motivation are essential. News media are an important factor here, as they are the major source of climate informa- tion for most citizens [1], raising their understanding and shaping their perception [2], as well as impacting behaviour [8]. Politically, news media set the policy agenda [3, 4] and influence public debate [5, 9]. Moreover, climate change is fundamentally a global issue and not limited to country bor- ders. It requires sustainable collaboration between countries and a consistent international

approach. On the other hand, the audience of newspapers is very much constrained by country borders. To stay updated on national news, a national newspaper can be a key source but it usually provides a narrower and more local perspective. On top of that, newspapers have a ten- dency to report from a partisan political perspective [10]. As much as climate change is not constrained by geographical borders, it does not affect a single side of the political arena. How- ever, this does not withhold politicians from vigorously debating the topic and related news becoming more and more politicized [11]. In short, news media are a major information source on climate change, in spite of their local and political character. These two notions together form the motivation for this study. With the problem of climate change and its solution being global, uncovering the differences in newspapers’ coverage of climate change can give insights into the underlying political pro- cesses. That makes it relevant to determine whether such variations exist and what they are. This study aims to uncover differences between articles on climate change from English- language newspapers around the world. To deal with the lurking trade-off between thorough- ness and scope, the potential of supervised machine learning is introduced. This allows to automatically analyze a large amount of data and consecutively reveal striking differences within it (see, for example, [12, 13]). Moreover, this data-driven approach can broaden the

field, by revealing differences that might not be picked up by experts in manual analysis. Specifically, the goal of this study is to detect topical and terminological differences between international newspaper articles on climate change. Since several aspects can lead to such dif- ferences, three characteristics are used to discriminate between the articles which thus become the prediction targets of the machine learning models. These are (1) the country of the newspa- per, (2) the political orientation of the newspaper and (3) the COP meeting occurring during article publication. For their annual occurrence, the COP meetings are an ideal reflection of the moment of publication. Moreover, the meetings boost the publication of climate-related articles [14]. Machine learning is used to automatically conduct the analyses. Each aspect is separately analyzed through classification. Since COP meetings have a temporal nature, this aspect is also analyzed with regression. The resulting models are intended to gain more insight into key dif- ferences between articles. Hence, human-understandable features are used within statistical PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 2 / 27 Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist.