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

they appear as significant for a given class. It may be that a feature that is significant to the model is not so apparent to a human reader, e.g., due to its context or frequency. We believe this can also be overcome by further analysis, be it using a deeper analysis of the dataset we cre- ated, or using a broader comparison with other findings. We see analysis of most informative features yielded by machine learning models as a useful addition to the field of climate change. We would like to encourage researchers in this field to incorporate the techniques we presented into their own qualitative studies. This could improve reliability and uncover patterns that may otherwise remain unseen by the human eye. We do not mean to suggest that manual analysis can be entirely replaced by machine learning. Rather, we believe the two can complement each other. A bottom-up approach such as the one we have presented, in which the content is leading, can prompt researchers to start looking for dif- ferences they might overlook in top-down analyses. Second, a more technical limitation of this proof-of-concept approach is the lack of parame- ter tuning for both the classification and regression models, e.g. using an RBF instead of a lin- ear kernel, or optimizing C for the support vector machines classifier. We have focused on collecting the data and optimizing the preprocessing of features, but have deliberately chosen not to involve the parameters of the employed LinearSVC and LinearRegression esti-

mators. It may well be that parameter tuning can further enhance the performance of the mod- els and can thus lead to more representative features. We therefore highly recommend incorporating even the mildest parameter tuning into future work. We also believe that the preprocessing strategy designed in this study can further be improved upon. For example, features that differ e.g. orthographically, lexically and geographi- cally, and are currently used by the model to distinguish countries should be filtered out more effectively. Named entity recognition also leaves room for improvement, which can possibly be achieved by using more recent, larger (transformer-based), and news-related models from SpaCy. Lastly, our systematic data collection approach was based on 3 co-occurring keywords extracted from Wikipedia articles, to raise relevancy towards climate change. It can however not be guaranteed that all collected articles explicitly cover the topic. Ambiguity of keywords and tricky keyword combinations may have captured articles on other subjects as well. We deliberately chose not to further reduce the size of the dataset by filtering for 4 co-occurring keywords, aiming to balance accuracy and data size. It was considered that a larger amount of data could mostly cancel out noise from less relevant articles. Despite the limitations that this study faced, we do consider it a successful proof of concept for feature analysis based on machine learning models in the field of climate change. We hope

that future work will overcome several of these limitations, but moreover that machine learn- ing will start to become a useful tool for researchers in analyzing textual patterns in climate change news and related documents. Supporting information S1 Appendix. Country selection. Detailed explanation of the country selection process. (PDF) S2 Appendix. Keyword selection. Figures detailing the keyword selection process. (PDF) S3 Appendix. Data description. Table of further statistics describing the data. (PDF) PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 24 / 27