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

Experiments. To determine an optimal configuration for the regression algorithm, a cou- ple of experiments were conducted. Since findings similar to those in the classification experi- ments were expected, the intermediate pre-processing strategies were not tested with the regression algorithm. The performance of the models was compared by use of all three error metrics, being MAE, MSE and RMSE. The error scores and R2 for the final model using the opti- mal configuration are in Table 6. Table 9 in S5 Appendix shows the scores from all experiments. Reproducibility All code employed, plots generated and most informative features yielded in this study are available on DataverseNL: doi.org/10.34894/AMISPR. The dataset was collected from Nexis Uni under license of the University of Groningen, and is therefore not publicly available. The data can be reproduced by licensees using the provided scripts. Results The most informative features yielded by the three classification models (COPs, countries and political orientations) and the regression model for COPs were analyzed to uncover what each model had learned from the data. The most positive and negative features for each class were listed, with the extremest coeffi- cients first. Herein, the positive features can be seen as characteristic of that class, and the nega- tive ones as uncharacteristic. For the COP classification model, this was limited to 50 features from each side of the spec- trum. A limit of 100 was applied for the political orientation and country models. This means

that 2,600, 600, and 200 most informative features were extracted from the three models respectively. The 250 most positive and negative informative features from the COP regression model were extracted, i.e. 500 in total. Seeing that a regression model only yields a single set of coeffi- cients, inherently not structured by class, it was useful to extract more features. These features were manually analyzed for topical and terminological differences. This was largely a qualitative analysis of the n-grams, subject to the scope of expertise and knowledge of the study’s authors. Some quantitative aspects were considered in this analysis as well, such as the height of the corresponding coefficient and the overlap of features across classes. For regres- sion, coefficient height played a larger role, as to comprehend the single set of features yielded. The analysis below provides an exploration of the features aiming to answer the research questions and assess the potential of the methodology, including finding room for future improvements. Apart from the analysis, the full lists of most informative features must in itself be seen as an important result of this study. These can be accessed through OSF as found under Repro- ducibility. Extended details about the performance of the classification and regression models can be found under Method in S5 Appendix. Table 6. Error scores of the final regression model for COP meeting numbers. MAE MSE RMSE R2 COP meeting 2.95 16.59 4.07 0.65 https://doi.org/10.1371/journal.pone.0297644.t006 PLOS ONE

Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 14 / 27