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

orientation prediction [13] for tweets. However, informative features are regularly seen as a byproduct and are presented in word clouds [13] or plots [12]. It is considered worthwhile to focus more on these informative features and perform a deeper analysis of them. Classification models may represent more general trends than manual analyses can capture and may contain details that human analysts could potentially overlook. This can come to its full potential when systematically collected cross-country data is compared along geographical, political and temporal axes. Data & method the goal of the study was to analyze differences between articles from several countries, politi- cal orientations, and COP meetings. First, a computational data selection strategy was employed to find features inductively at scale. Subsequently, the selected data was preprocessed in order to filter out many overpredic- tive features. To allow the machine learning models to be evaluated on unseen data, separate training and evaluation sets were created. As the last preparation step, features were extracted using the techniques described below. Then, several classification models were trained and a regression model was developed to predict the COP meetings. Finally, the most informative features were analyzed. Data A unique and entirely new dataset was created to analyze international newspaper articles on climate change. The data was collected through Nexis Uni (internationalsales.lexisnexis.com/products/ nexis-uni), the research platform from Lexis Nexis. Under license, this platform provides

access to full-text publications from over 1,000 newspapers. As a first step, a selection of newspapers was made. Only English-language newspapers were selected, to facilitate employment of statistical machine learning algorithms and feature interpretation by the authors. English is the most spoken language across the world and has the third most native speakers (ethnologue.com/insights/most-spoken-language/), guarantee- ing sufficient coverage. The G22 was used to narrow down the scope to sufficiently impactful countries. This group of 22 countries was mostly constituted based on financial impact (imf.org/en/About/ Factsheets/A-Guide-to-Committees-Groups-and-Clubs#GS). Six countries from five conti- nents were chosen: (1) Australia (Australia), (2) Canada (North America), (3) India (Asia), (4) South Africa (Africa), (5) the United Kingdom (Europe) and (6) the United States (North America). This was based on geographical spread, availability of English-language newspapers, and their accessibility through Nexis Uni. However, since the distribution of the data featured several important irregularities (see S1 Appendix), only articles from a subset of countries (Australia, Canada, and the United King- dom) were eventually selected for the analysis. Since these countries are all from different con- tinents, the subset was still suitable to answer the research questions. Each country was represented with newspapers from each end of the political spectrum (e.g. liberal/conservative) to reflect political orientation. The knowledge of the authors was

used to assign these labels, substantiated by analyses from Media Bias Fact Check (mediabiasfactcheck.com) and AllSides (www.allsides.com/media-bias/media-bias-ratings) for the orientation labels. Both sources categorize newspapers using one out of five labels (left, left-center, center, right-center, right). The end result was a selection of 7 newspapers, shown PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 7 / 27