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

The latter is in line with findings by Keller et al. that climate change news is individualizing, with a growing focus on individual actions [14]. Apart from features, this study has yielded a new dataset that can be analyzed in many ways to uncover new insights concerning newspapers’ approaches to climate change. The data also lends itself for e.g. headline generation or manual qualitative analyses. Future research could for example make use of neural networks [45] to outperform our current models or employ word embeddings [44] to predict the political orientation of the newspapers. On the other hand, studying the data through the use of manual discourse analysis will help unfold different insights that automated methods struggle to detect, e.g. some contextual characteristics of the corpus. All in all, the research constitutes a first step in exploring the automatic detection of differ- ences in news articles with regard to countries, political orientations, and COP meetings. The study contributes to the use of supervised machine learning for highlighting discriminative features on how newspaper articles approach climate change globally. Future works should pick up where this study left off. Above all, we consider it essential to further bridge our results to a wide array of previous works, and see plenty of opportunities to do so. For example, studies focusing on scientific publications [6, 38, 41], government policies [7, 39, 42] or news articles [11, 14–17, 26, 30, 31, 35]. It will be insightful to put the features

yielded in the study at hand further into context and to see whether and how results align with earlier findings. Furthermore, future works should expand our analyses to other countries and news articles from the most recent COP meetings (26, 27 and 28). Now that the feasibility of the approach has been proven, we also consider it worthwhile to focus on further finetuning machine learn- ing model development in ways we did not cover, e.g. by tuning the SVM’s hyperparameters (scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) and using cross-validation (scikit-learn.org/stable/modules/cross_validation.html). The creation of a protocol for feature analysis should also be taken into account, to further structure annotation of the yielded most informative features. Another opportunity lies in more directly comparing feature overlap from our classification and regression models for COP meetings. Limitations While our exploratory research has yielded interesting insights into how news media are approaching climate change across countries, we also acknowledge several limitations that accompany our investigation. First, we consider machine learning a way to reduce the chance of introducing human bias in the analysis of newspaper articles and have presented the technique as such. Our methodol- ogy proves that it is indeed possible to systematically find, collect, and digest articles using clas- sification and regression models. Nonetheless, the analysis of these features was a manual process, based on our own general knowledge and common sense. We are convinced that

more systematic approaches to analyzing the meaning and context of features yielded by our models can be designed. For example, we strongly suggest bridging our results with findings by preceding publications on climate change news [11, 14–17, 26, 30, 31, 35], scientific publi- cations [6, 38, 41] or government policies [7, 39, 42]. Also, we stress the need to pair the fea- tures we present with the knowledge that experts in the field of climate change have. It must also be pointed out that the machine learning methodology presented in this study does not take into account context beyond the features themselves. Word order is only pre- served within multigram features. Beyond that, it is impossible to determine from the model itself how certain most informative features are used in the original text and substantiate why PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 23 / 27