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

integrated in the existing pipeline and the approach to preprocessing, splitting the data and generating features remained the same consequently. The COP meeting labels were converted to numbers. COP6a was represented as 6.5 and all other meetings as their round edition number. The models were then trained using similar n- gram feature combinations as in the classification experiments. The performance of the resulting models was evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Like for classification, the most informative features were extracted by linking the model’s coefficients to the original features. For their nature to predict continuous numbers, regression models only yield overall coefficients. These are less insightful than the per-class coefficients from classification. However, the coefficients do indicate what drew the model towards a higher or a lower prediction. Unlike the classification model, no baseline was defined for regression. A rule-based regres- sor like DummyRegressor (scikit-learn.org/stable/modules/generated/sklearn.dummy. DummyRegressor) relies on always predicting a single value, such as the mean or the median of the labels. Having as many as 26 COP meetings that moreover have a temporal nature, such a model would inevitably produce high errors that are effectively meaningless. Table 5. Classification report of the final model for COP meeting labels. precision recall f1-score COP1 0.58 0.37 0.45 COP2 0.46 0.30 0.36 COP3 0.41 0.52 0.46 COP4 0.51 0.49 0.50

COP5 0.34 0.24 0.28 COP6 0.38 0.23 0.28 COP6a 0.41 0.35 0.38 COP7 0.41 0.39 0.40 COP8 0.56 0.23 0.32 COP9 0.25 0.22 0.23 COP10 0.33 0.13 0.18 COP11 0.43 0.27 0.33 COP12 0.39 0.50 0.44 COP13 0.42 0.49 0.45 COP14 0.46 0.47 0.47 COP15 0.50 0.66 0.57 COP16 0.51 0.34 0.41 COP17 0.42 0.43 0.42 COP18 0.56 0.41 0.47 COP19 0.48 0.49 0.49 COP20 0.54 0.49 0.51 COP21 0.46 0.76 0.58 COP22 0.53 0.49 0.51 COP23 0.63 0.42 0.50 COP24 0.59 0.59 0.59 COP25 0.53 0.74 0.62 Macro avg 0.46 0.42 0.43 https://doi.org/10.1371/journal.pone.0297644.t005 PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 13 / 27