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

The collection took place between 23 July and 4 September 2020, in reverse order of COP meetings, starting with COP25 and ending with COP1. Next, the plain text of each article was extracted from the collected PDFs. This was achieved using Python’s pdftotext (github.com/jalan/pdftotext) library. The result was sanitized by remov- ing (1) unwanted newlines, (2) multiple consecutive spaces and (3) page headings and footers. All elements of the article were separately extracted, being (1) the newspaper (2) the date, (3) the headline, (4) the body text and (5) classifications by Lexis Nexis. This data was stored in 26 JSON files, one per COP meeting. Together, the full dataset for six countries contained 337,777 articles, on average 12,991 per COP meeting. 4,309 PDF articles could not be pro- cessed, 1.3% of all articles. Fig 2 in S2 Appendix shows the distribution of articles over COP meetings. Apparent is the overall increase of article counts over the years. At this point, the dataset contained matches for at least one of the keywords. However, a single occurrence of a keyword (like “ocean”) does not guarantee a relation to climate change. Therefore, a procedure was used to select all articles that contained at least 3 of the selected keywords. The full procedure is explained below. Scatter plots of absolute and unique keyword matches per COP meeting showed that key- word matches were highest for the earlier retrieved articles. Exemplar scatter plots can be seen in Figs 4, 5 in S2 Appendix.

Keller et al. [14] have shown a search strategy with multiple co-occurring keywords is use- ful. Hence, articles that contained less than 3 unique keywords were discarded from the data- set. While this could not guarantee matches solely covering climate change, it was considered that chances increased substantially by applying this approach, without disproportionately reducing the size of the dataset. Filtering by 3 co-occurring keywords reduced the full dataset to 62,824 articles, on average 2,416 per COP. Fig 3 in S2 Appendix visualizes this reduction. A threshold of 4 keywords was also considered, but this was discarded as it reduced the dataset to only 31,650 articles. Eventually, given the irregularities observed in the full dataset (S1 Appendix), only the 43,572 articles from Australia, Canada, and the United Kingdom were used for model develop- ment, shifting the average number of articles per COP to 1,676. More details are specified in Table 7 in S3 Appendix. Three article label categories were then needed: (1) the country of the newspaper, (2) the political orientation of the newspaper, and (3) the COP meeting. The articles were already bundled per COP meeting. The former two labels could directly be derived from the publish- ing newspaper and assessments by Media Bias Fact Check and AllSides. Table 2 shows the label distributions for the eventual dataset. The outstanding Australian coverage observed by Holt and Barkemeyer [16] is not seen here. Nonetheless, the overall

increase over time that Schmidt et al. [17] and Keller et al. [14] described, is apparent in the data- set at hand, which is also clearly visible in Fig 3 in S2 Appendix, which visualizes the full dataset. Preprocessing To further optimize the data for machine learning, extensive preprocessing was employed. This entailed filtering the contents of the articles, rather than discarding articles in their entirety as in previous phases. The preprocessing involved 8 individual filtering steps which were crafted partly during intermediate training runs of the model, as shown in S4 Appendix. Most preprocessing relied entirely on case-insensitive regular expressions to find and remove unwanted phrases. Named entity removal relied on SpaCy’s (spacy.io) tokenization, allowing to filter the data token by token. The tokens were merged into a single string again afterward. PLOS ONE Learning from climate change news PLOS ONE | https://doi.org/10.1371/journal.pone.0297644 March 20, 2024 9 / 27