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

societal dimension of climate change” (p. 10). However, their study also points to important differences between countries of the Global North and the Global South, such as the first focus- ing more on climate science while the second paying more attention to the impact of climate change on people’s lives. Stecula and Merkley [35] are among the few researchers who have employed supervised machine learning with support vector machines (SVMs) in the field of climate change news. They collected a dataset from four US news sources, being newspapers (1) The New York Times, (2) The Wall Street Journal and (3)The Washington Post, plus the news wire agency (4) Associated Press. 14,141 articles were collected from Lexis Nexis and Factiva. 2,177 of them were annotated for using economic costs, economic benefits, conservative ideological and uncer- tainty frames. Four SVM models were then trained on 80% of these, one model for each type of frame. The classification tasks were binary, either using or not using the frame. This led to accuracies around 80% across the board. The results of the SVM were then used to quantita- tively analyze the share of stories that featured each frame. This showed that economic cost frames coincided with policy debates, such as COP meetings. There also appeared to be a downward trend for economic cost, against an upward trend for economic benefits. The Wall Street Journal appeared to differ from the other three sources. It featured most economic cost and conservative ideological frames. In general, conservative ideological and uncertainty frames

appeared to be quite uncommon. The latter moreover turned out to be on the decline. Despite the works of Stecula and Merkley [35], Keller et al. [14] and Chinn et al. [11], the use of ML is still rather slim in the field of climate change news. In adjacent climate change fields, there have been works that used machine learning to select scientific publications related to climate change [6, 38, 41] or to automatically categorize government policy docu- ments, e.g. into adaptation-related classes [7, 39, 42]. Employing supervised models to uncover concrete linguistic differences between countries, political orientations or COP meetings is still unexplored. The study at hand considers that analyzing the informative features of classifi- cation and regression algorithms can be a useful contribution to the field. Doing so can help to uncover detailed differences in a large collection of climate change-related articles from a wide range of countries. In sum, there seems to be a gap in the field of climate change news analysis, which machine learning can partly fill up. Manual analysis is currently the dominating approach, yet it suffers from some limitations: it introduces the trade-off between thoroughness and scope and comes with the risks of missing out on important data-ingrained features. Supervised machine learning followed by informative feature analysis can help overcome these limitations. Wherever this technique has been applied so far, it was used chiefly for the prediction of artificial labels, rather than feature analysis. Classification and regression algo-

rithms are capable of processing large amounts of data. More or less independent of the data size, by having specific training objectives, classification models can reveal data patterns that can relatively easily be analyzed. It must be noted that the interpretation of most informative features remains a manual pro- cedure. However, incorporating supervised machine learning into the analysis of the corpus postpones this risk until the very last phase. It also enables analysis of a larger corpus in a con- siderably shorter amount of time and ensures features are processed entirely systematically. This distinguishes feature analysis based on machine learning from manual analysis and makes it a fundamentally different means of studying climate change news than has been used so far. Despite the potential, machine learning is not a flawless method. It should therefore be used to complement human analysis, which is more elaborately addressed under Limitations. The potential of analyzing models in such a way, i.e. extracting the most informative fea- tures, has already been shown in various studies, e.g. on author profiling [12] or political PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 6 / 27