Ideological biases in social sharing of online information about climate change

Ideological coding of source domains To examine ideological positioning of popular source domains along axes of political leaning (from left/progressive to right/conservative) and climate scepticism (from environmentalist to sceptic), 62 of the 75 most commonly shared source domains across the entire study period were manually coded for ideological bias (listed in S4 Table in S1 File). Ideology expressed in articles from each domain was graded on a three-point scale for political opinion (Left-Neu- tral-Right) and climate change opinion (Environmentalist-Neutral-Sceptic), with an additional null rating (Unclear) added to both scales for cases where no clear ideology was seen. A team of six human coders used this scale to independently score text extracts of articles/ content from the most commonly shared domains. For the 75 domains with the most shares across the seven-week period, we sampled the page content for five most shared articles (using Diffbot to extract clean text, as described above). If fewer than five articles from a popular domain were available in the dataset, then all were included. In some cases, the page formats were incompatible with the Diffbot API, returning no content for 10 domains (see S4 Table in S1 File for the excluded domains). We additionally excluded the social media sites Twitter, Wordpress and Reddit; these do not have editorial control and therefore lack a unified ideologi- cal position. This left a set of 62 domains to be coded. Each extract consisted of up to three

complete paragraphs (of at least 30 words) from the linked web page text. To limit any subjec- tivity arising from the coders’ personal perspectives, each extract was anonymised (i.e. source domain and author information were removed) when presented for coding. Coders were provided with the following definitions to help make their assessments: • Left: A left-wing stance can be characterised by the promotion of state benefits and services, public investment in and regulation of private businesses, increased taxation of corporations and high earners, and support for workers and trade unions. • Right: A right-wing stance can be characterised by promoting low taxation and minimising the interference of government in personal and business lives. Public investment is mini- mised, in favour of allowing market forces to control growth and provision of services. • Environmentalist: An environmentalist stance supports the scientific consensus on anthro- pogenic climate change and promotes immediate action by governments and individuals to mitigate the future impacts. • Sceptic: A climate sceptic stance opposes the scientific consensus on anthropogenic climate change. Such opposition varies from questioning the existence or causes of climate change, to opposing efforts to mitigate its impacts. Each coder assigned a score to each article extract independently. That is, six coders gener- ated up to five scores for each of 62 domains (in practice, this amounted to 1698 scores out of a maximum of 1860, with each domain receiving up to 30 scores). Each article score is an integer

denoting the level of ideological bias the coder observes in that article (-1/0/1 for left/neutral/ right or environmentalist/neutral/sceptic, in order, with unclear scores ignored; see S3 Table in S1 File). The article scores assigned by each coder were then averaged for each domain to determine an overall domain score for that coder. Each domain score is a real-valued num- ber in the range [−1, 1]; there are 6 scores for each domain, one from each coder. Since the assignment of ideological bias is somewhat subjective, we performed an adjust- ment to the domain scores assigned by each coder. This adjustment normalised each domain score by subtracting the mean domain score for that coder across all 62 domains, then dividing by the standard deviation. This process represents domain scores as z-scores and normalised for subjective bias of individual coders. Finally, we average the normalised domain scores PLOS ONE Ideological biases in social sharing of online information about climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0250656 April 23, 2021 9 / 25