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

from detecting an overall increase of climate change attention over time, Schmidt et al. [17] also found cross-national differences in the attention levels. Further analysis of these differ- ences indicated that more vulnerable countries do not necessarily deviate from the average. Additionally, countries that have obligations under the Kyoto Protocol also have more issue attention to climate change on average. This approach yields, however, similar concerns as the study by Holt and Barkemeyer [16]: word frequency is certainly interesting, yet it cannot serve as a full representation of content just on its own. When applied correctly, this is something machine learning can overcome. Keller et al. [14] were among the first to employ machine learning (ML), albeit unsuper- vised. They analyzed Indian newspaper articles for 28 topics related to climate change. The authors collected 18,224 articles from the two largest Indian newspapers, published between 1997 and 2016. In line with previous research in the field [15, 17], three keywords were used to find the articles, being climate change, global warming and greenhouse effect. They first assessed the proportion of articles dealing with climate change. Then, topic modeling with LDA was applied using unsupervised machine learning. The authors found that peaks in reporting on climate change strongly coincide with COP meetings (unfccc.int/process/bodies/supreme- bodies/conference-of-the-parties-cop). Overall, they revealed that reporting on climate change

increased substantially between 1997 and 2016. Nonetheless, the attention in India still lagged way behind compared to other countries. Furthermore, they identified 28 climate change- related topics. Agriculture was the most popular at 5.02%. These topics could be structured under four overarching themes: Climate Change and Society (27.37%) was most popular over time, followed by Climate Politics (23.12%). In recent years, both became slightly less common, while Climate Change Impacts (17.75%) became more frequent. The less popular Climate Change Science (6.24%) saw rather few fluctuations. Lastly, they observed the individualization of climate change news, showing that more and more attention is drawn to individual effects and actions. Unsupervised machine learning was also used by Chinn et al. [11]. The authors built a data- set of newspaper articles published between 1980 and 2017 in the United States, covering both national and regional newspapers. The articles were retrieved from Lexis Nexis by searching for the Environment tag and annotated using seven keyword dictionaries on different climate- related topics. The labeled dataset was then used to study the politicization of climate change news. Mentions of Republicans and Democrats were quantitatively compared to mentions of scientists. Their results show an increase for politicians, against a decrease for scientists. In fact, these two frequencies crossed between 2000 and 2010; every article since 2000 on average con- tained at least one mention of politicians, confirming the politicization trend. Additionally, the

authors employed unsupervised machine learning to study polarization. First, partisan para- graphs on climate change were identified, i.e. mentioning either Republicans or Democrats. Wordfish (tutorials.quanteda.io/machine-learning/wordfish/) [40] was then used to determine a score for the linguistic differences between the two paragraphs. This was done for all included two-year periods corresponding to the terms of US Congress, enabling comparison over time. Results showed consistent linguistic polarization until 2011, but an increase after- ward, as shown by the mentions of Republican terminology, which overtook coverage of Dem- ocratic phrases. More recently, Hase et al. [26] conducted a novel longitudinal automated cross-country analysis of news media across the globe. They compared coverage in ten countries from the Global North and the Global South by employing unsupervised machine learning. Their study revealed both similarities and differences in coverage of climate change. Political, scientific, and (partly) societal focusing events were related to small mounts in global news media inter- est and countries from both areas reported on “ecological changes, climate science and the PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 5 / 27