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

• Causes Where there are effects, there are causes. Logically, several of these were found within the features for the COP meetings. Again, it was hard to notice a blatant trend in these. The explosion for COP25 was not observed either. Rather, more features were found for interme- diate meetings like COP20 and 21. It may be that causes were not specifically addressed before and are more left to common sense by now. In extension, the attention may increas- ingly be drawn towards measures against climate change. 03: diesel {, coal+, petrol { 04: coal { 15: pipeline { 20: shale oil+, methane+, gas {, industrial { 21: fracking+, fuels+, transport {, fossil+ 22: pipeline+, coal+ 23: diesels+, plastic+ 24: gas { 25: car { • Measures Looking at measures, none were found for earlier COP meetings. This could support the idea that the attention is shifting from observing causes to acting with measures. COP21 clearly shows solar and clean energy as indicative. COP23 features energy storage. By COP25, clean energy had become a negative feature, thus being uncharacteristic for the class. Instead, transition, electric and emissions reduction had become positively indicative, i.e. characteris- tic for articles published around COP25. 20: reduce { 21: solar+, clean energy+ 23: energy storage+, uranium { 24: power stations { 25: transition+, electric+, clean energy {, emissions reduction+ Only 3% to 19% of the extracted most informative features were deemed relevant to climate change and have thus been analyzed (3: 14, 4: 7, 15: 17, 20: 14, 21: 18, 22: 10, 23: 14, 24: 3, 25:

19). While most features cannot be plausibly explained, several can be related to historical events co-occuring with COP meetings. Examples are the Ottawa Treaty (un.org/ disarmament/anti-personnel-landmines-convention/) on land mines (COP3), the Ebola pan- demic in Western Africa (COP20), the advance of Islamic State in Iraq and Syria (COP21), the presidential election of Donald Trump (COP22), the mass displacement of the Rohingya (COP23), the Brexit agreement (COP24), and the impeachment of Trump (COP25). All fea- tures can be accessed, as indicated under Reproducibility. Apart from a classification model, the COP meetings were also predicted using regression. While the classification model had yielded rather low scores, the regression model seemed to be performing quite well. This results from the fundamentally different approach of the two algorithms and the related means of evaluation. Whereas a prediction of a class can only be wrong or right, a numeric prediction by regression is evaluated with its precise error from the gold standard. Interpreting the MAE of 2.95, one can say that the regression model is on average roughly 3 COP editions off. RMSE corrects for larger variance and is 4.07 for this model. This can PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 20 / 27