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

Certainly, machine learning can play a pivotal role in the analysis of vast climate change- related datasets. This is because machine learning enables the input of vast volumes of data into a computer algorithm, empowering the system to examine this data and generate data- driven recommendations and decisions solely based on the input information (see, for exam- ple, [6, 13, 36–39]). Consequently, the utilization of machine learning helps reduce human intervention in data manipulation and reduces the chance of introducing biases. Grundmann and Scott [15] performed one of the first automated content analysis studies into the discourse of climate change over time. They used newspapers from France, Germany, the UK, and the USA. A corpus of 106.5 million words was created, by retrieving articles pub- lished between 2000 and 2010 from Lexis Nexis. The peaks and troughs of attention for climate change were visualized per country. Furthermore, keyword analysis such as collocation analysis was also employed. The goal of the research was to study which terminology each country favored to describe the concept of climate change. The authors also compared the attention paid to advocates and skeptics and analyzed the influence of Climategate (factcheck.org/2009/ 12/climategate/). The study revealed clear differences between the climate change terminology among the four countries. The study also showed that advocates were favored over skeptics in all countries, but more in Germany and the UK than in France and the USA. However, this

study could methodologically only rely on manual detection of climate change-related key- words, risking introducing human biases and missing out on other potentially relevant aspects of the data. Holt and Barkemeyer [16] pioneered the broadening of the geographical scope, as they con- sidered 112 newspapers from 39 countries. This is one of the largest samples ever considered, also according to Schmidt et al. [17]. Articles were published between 1990 and 2008 and were in seven languages. For each newspaper, the monthly averages of hits on the search keywords were calculated. These allowed to quantitatively compare the coverage of sustainability and cli- mate change over time. The analysis showed clear peaks for sustainability around the confer- ences of Rio de Janeiro (1992), Kyoto (COP3, 1997), and Johannesburg (2002). For both sustainability and climate change, the trend was upwards. The latter term showed a sudden upsurge after 2007. Furthermore, the authors identified several interesting local phenomena, such as the Australian coverage being up to five times larger than the global average and the US trend being downward, thus opposing the global one. Moreover, fluctuations over time were successfully interpreted by means of the issue-attention cycles and punctuated equilibrium models. This highlighted events such as the release of Al Gore’s movie An Inconvenient Truth in 2006 and the financial crisis starting in 2008. While these are interesting insights, the size of the dataset left the authors seemingly no choice but to use a quantitative representation of cov-

erage. Certainly, they could relate peaks and troughs to events, but frequency alone can never reveal nuances within the text. Fewer publications on climate change can for example still imply more factual content, featuring less skepticism. This asks for a deeper analysis of the contents, yet without having to give up on data size. In yet another quantitative study, Schmidt et al. [17] employed newspaper articles from 27 countries, including both countries with commitments to the Kyoto Protocol and countries that suffer strongly from the consequences of climate change. Where possible, two newspapers were incorporated from each country, to reflect different political orientations. The relevant articles from these newspapers were retrieved by using a complex search string, aggregating a wide range of terms related to climate change. In doing so, the selection was more fine- grained; articles that covered climate change without literally mentioning the term could be included while preventing noise from articles that used words like temperature or heat in another context. In the study, issue attention level, defined as the attention for climate change- related topics compared to the attention for other issues, was handled as a key metric. Apart PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 4 / 27