Ideological biases in social sharing of online information about climate change

certain aspects of climate science. This is the only community to focus on former US politicians Obama and Gore and the phrase global warming dominates the bigram cloud (Fig 6c). Community 3 This community consists of many mainstream domains revealing a US focus. The most prominent domains here are established mainstream media sources such as the Washington Post and New York Times. As with Community 1, the Paris Agreement on climate change is a key topic of interest. Community 4 This community includes a number of alternative and smaller news media domains with a mostly left-wing bias, such as Daily Kos and Mother Jones, amongst established mainstream news sources. The content here is similar to that of Community 1 and Community 3, but also references then US Environmental Protection Agency Administrator Scott Pruitt and Michigan congressman Tim Walberg for their comments around climate change. Community 5 This community is comprised of a mix of social media, news and commentary sites. Again, the Paris Agreement decision is a focus, with additional framing around global consequences and opinion- pieces on the decision. https://doi.org/10.1371/journal.pone.0250656.t002 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 16 / 25

from which content was shared show higher similarity between weeks in Table 3c. User popu- lations show limited persistence between weeks, summarised by Table 3a. The overall pattern is that, from week to week, a limited proportion of the user population and set of source domains persist, with much lower persistence in the sets of articles that are shared. This is an interesting finding with respect to the high consistency in the two-cluster network structure that is reliably observed every week. The lack of persistence in shared URLs may be explained by the general volatility of news media, where articles typically have a short lifetime (e.g. 2-3 days visibility in online sharing [13]). Moderate persistence of users and sources between weeks perhaps suggests an active core group who are present each week, with a wider group who appear less frequently. A marked difference can be observed in the typical similarity scores for Week 4 and for other weeks. Lower similarity scores were observed for users, URLs and domains from Week 4 appearing in other weeks, while conversely, higher similarity was observed for users, URLs and domains from the other six weeks appearing in Week 4. The number of new users, URLs and domains in Week 4 also shows a stark contrast with other weeks: 70.1% of users, 82.0% of URLs and 47.9% of domains are unique to Week 4 and not present in any other week. This is strong evidence that the events of Week 4 appear to have spurred an influx of both social media participants and digital media sources (users: mean 49.3%, min. 45.6%, max. 51.2%,

Table 3. Similarity scores for users, URLs and domains between each of the seven weeks calculated using Eq (3). Similarity is directional. The similarity given in cell (4, 1) is the proportion of the users/URLs/domains in Week 4 also in seen Week 1. Cool shades indicate values smaller than the mean while warm shades indicate values greater than the mean. Week 1 2 3 4 5 6 7 1 0.29 0.33 0.39 0.25 0.25 0.22 2 0.24 0.30 0.35 0.22 0.24 0.21 min.(S) = 0.084 3 0.19 0.21 0.38 0.20 0.19 0.18 mean.(S) = 0.236 4 0.08 0.09 0.15 0.11 0.10 0.09 max.(S) = 0.389 5 0.18 0.20 0.25 0.36 0.24 0.21 stdev.(S) = 0.078 6 0.19 0.22 0.26 0.35 0.26 0.25 7 0.20 0.23 0.28 0.36 0.27 0.30 (a) Asymmetric overlap of users. Week 1 2 3 4 5 6 7 1 0.13 0.08 0.11 0.06 0.05 0.04 2 0.10 0.14 0.10 0.05 0.05 0.04 min.(S) = 0.022 3 0.05 0.10 0.16 0.05 0.04 0.03 mean.(S) = 0.078 4 0.02 0.03 0.06 0.06 0.03 0.02 max.(S) = 0.193 5 0.04 0.05 0.06 0.19 0.16 0.07 stdev.(S) = 0.047 6 0.04 0.05 0.06 0.10 0.17 0.16 7 0.04 0.04 0.05 0.09 0.09 0.19 (b) Asymmetric overlap of URLs. Week 1 2 3 4 5 6 7 1 0.55 0.59 0.72 0.58 0.54 0.51 2 0.44 0.58 0.75 0.48 0.55 0.48 min.(S) = 0.155 3 0.34 0.41 0.76 0.42 0.42 0.39 mean.(S) = 0.496 4 0.15 0.20 0.29 0.23 0.22 0.18 max.(S) = 0.767 5 0.41 0.43 0.53 0.76 0.52 0.46 stdev.(S) = 0.158 6 0.41 0.52 0.56 0.76 0.56 0.53 7 0.46 0.54 0.62 0.77 0.59 0.63 (c) Asymmetric overlap of domains. https://doi.org/10.1371/journal.pone.0250656.t003

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Fig 7. Consistent network structure over time. Network diagrams of the top five communities across the six remaining weeks. Each figure is oriented such that the left-wing cluster is on the left and the right-wing cluster is on the right. In each case node colour signifies community membership and size is proportional to the square root of total share count. Communities are labelled 1 − 5 in decreasing order of size, and colored blue, yellow, green, red and purple respectively. Node placement is determined by the Python implementation of the ForceAtlas2 algorithm [50]. https://doi.org/10.1371/journal.pone.0250656.g007 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 18 / 25

σ = 2.10%; URLs: mean 70.3%, min. 66.6%, max. 73.2%, σ = 6.54%; domains: mean 29.8%, min. 27.3%, max. 31.5%, σ = 4.21%). While web domains exhibit the most stability between weeks, URLs exhibit the least stability and users fall between these two extremes. To confirm that the persistent two-cluster network structures seen in Fig 7 are polarised along the same left-right political and environmentalist-sceptic ideological axes as that seen for Week 4 (see Fig 5), the domain bias codings were applied to the networks created for Weeks 1- 3 and 5-7 (S6 and S7 Figs in S1 File). Furthermore, average biases were calculated for all com- munities in each week in Fig 8. Trends in polarisation over time are shown as network aver- ages for political and climate change biases, alongside the community averages, in Fig 8. In most weeks, the whole-network average shows mild left-wing and environmentalist bias. Week 5 shows (for the only time across the seven-week period) a large neutral community. Supporting the visual evidence seen in S6 and S7 Figs in S1 File, and the community-level bias scores in Fig 8, these findings show that the polarised network structure observed in Week 4 is persistent and is not an artefact of the increase in activity in the climate change conversation despite the turnover in users, source domains and shared articles. Discussion This paper presents an analysis of digital media sharing behaviour around the contested issue of climate change. Our analysis looks at the network structure formed by users sharing web

articles related to climate change, combining network analysis with computational and human text analyses to identify and characterise communities of users and the articles/sources they Fig 8. Levels of political and climate change bias over the course of the seven week study period. These are measured as the mean coded bias of domains weighted by total shares (see Ideological coding of source domains). Bias in each of the five largest communities are represented by the scatter points in each week, and the bias across the whole network is given by the lines. The colour of the community points is consistent with other figures. In most weeks, the average network bias is left of centre and more environmentalist than sceptic. This trend continues to the individual communities, with the majority being left-wing and environmentalist. https://doi.org/10.1371/journal.pone.0250656.g008 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 19 / 25

share. The aim is to understand how people engage with, and share online media content about climate change on social media (specifically Twitter). We have found that amongst the communities of shared URLs, right-wing and climate sceptic views are strongly correlated, as are left-wing and environmentalist views. This correla- tion has been observed in individuals before using survey-based methods (e.g. [3, 51, 52]). Our study shows that the association extends to media outlets, specifically to the content produced by a large set of online news providers (Fig 4), and is a major feature of how online content is shared. Recent work by Hornsey et al. [52] suggests that the scepticism-conservatism link may be strongest in the US. This finding is supported here, where we observe two large communi- ties based mainly on US sources; one more left-wing and environmentalist, the other more right-wing and sceptic. Although we did not examine where the users in our dataset were located geographically, the dominance of US sources in two of the communities, and of UK sources in one of the communities, suggests that these communities are largely formed around users from those countries. Our overall focus on English-language tweets and media content, plus large numbers of Twitter users in these countries (nearly 15% of online US adults used Twitter in 2017 [12]), are consistent with this conjecture. In this study we find two levels of community structure. At the highest level, there is polari- sation and segregation with a large left-wing/environmentalist grouping and a smaller right-

wing/sceptic grouping clearly visible in the sharing network after the application of the force- directed layout algorithm. Within the left-wing/environmentalist group, algorithmic commu- nity detection finds (typically) four smaller groupings, characterised by further analysis as above. It is interesting to note that the right-wing/climate sceptic group is more densely con- nected internally than the left-wing/environmentalist group (Student’s t-test applied to average clustering coefficients of all left-wing and right-wing communities in Figs 3 and 7, with clus- tering coefficients calculated in Gephi using Latapy’s algorithm [53], p = 0.019). This finding echoes the observation by Colleoni et al. [54] that in the US, active Republicans exhibit greater levels of homophily than Democrats in their patterns of interaction on Twitter. The temporal analysis presented above gives confidence that the polarised network struc- ture is robust over time, despite turnover in the user populations, sets of shared articles and news sources. We studied similarity between the sets of users, URLs and domains across the seven weeks, as well as the proportion of users, URLs and domains appearing only in the exceptional Week 4 (in which climate change was the subject of a mainstream news event when President Trump announced the US withdrawal from the Paris Agreement). This major event substantially increased the volume of social media messages related to climate change and drew in an increased number of new users, who shared a greater number of articles from a

greater number of online information sources. Thus we observed network structure and com- position in both ‘normal operation’ as well as in an unusual state of high activity, showing the typical level of week-to-week volatility as well as the substantial change under the influence of the disruptive mainstream news event. At all times, including the disruptive event, the polar- ised network structure remains strong and clearly visible, despite substantial changes in its constituent parts (users and articles). As such, we conclude that the broad topological features visible in Figs 3 and 7 are stable over the seven-week duration of our dataset. Similarly, we find that the association of left-wing/environmentalist views, and of right-wing/sceptic views, are persistent, as shown in Fig 5a and 5b, S6 and S7 Figs in S1 File. Future study may find it fruitful to examine whether such stability exists for other online networks or other politicised discussions. The similarity statistics in Table 3 suggest a general trend for a small proportion of the users, URLs and domains to persist across weeks while others appear only sporadically. In the URL case, the comparatively low levels of similarity are not surprising as news articles are 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 20 / 25

quickly superseded by new facts and perspectives. Among the users and domains, the evidence suggests the network has a stable core with an unstable periphery. Over the seven weeks stud- ied, a number of websites establish themselves as critical to the flow of information around cli- mate change, either as news sources (such as The Guardian or Breitbart) or as conduits for personal opinions (such as Twitter and Wordpress). Many other sites are peripheral to the news-sharing network of climate change, appearing sporadically with a lower frequency of usage. This paper has deliberately avoided studying users as individuals, but it seems reason- able to expect a core group of committed and strongly interested people who regularly share information about climate change, with others who contribute less frequently or only when motivated by external factors. Considering this user behaviour in the context of the persistent network structure we have observed, it appears that the revealed polarisation is a feature of the system as a whole and not caused by specific events or users. This article illuminates several new dimensions of the media debate around environmental politics and climate change. The findings complement previous studies that have shown cli- mate-related echo chambers exist in direct user-user interactions (e.g. [18, 27]) by showing that similar structures also characterise patterns of information-sharing. The Twitter messages that form the sharing network studied here reflect a mechanism of news promotion and active

attempts to inform others, which is not always the case in direct personal interactions. We also capture a vital additional element by exploring the network topology over time and demon- strating that the polarised structure persists, even when there is a disruptive mainstream news event. In addition, we have shown a strong correlation between political and climate-related ideological biases in news production and consumption, with associations between left-wing/ environmental and right-wing/sceptic positions. This widens the scope of previous studies of mainstream news media (e.g. [51]) to include the increasingly important online news media. When looked at in isolation, several domains appear to be coded with more of a left-wing, environmentalist bias than would be expected by domain knowledge experts. Typically neutral sites, such as Bloomberg, are reported as left-wing and environmentalist primarily through the lenses they use to cover the specific topic of climate change. Other domains, such as the Daily Caller, may have been poorly represented by the content sampling process as factual and scien- tific extracts taken without surrounding challenging context were considered as supporting the scientific consensus. This “good faith” on the part of the coders returns a particularly erro- neous response for Watts Up With That, a prominent blog in the sceptical community. Many of their articles present quotations from public figures and scientific papers in addition to their own commentary that frequently challenges the framing, which are contextual clues that may

be unavailable to the coders. The last explanation for possible misclassification of certain domains identified in this exercise is the reposting of content from other sources. Investigation of the articles coded from Fox News found that three of the five articles were from Associated Press sources, which may present a different editorial bias to Fox News original content. The choice of which content to repost from other sources is an important editorial decision and as such we believe this inclusion has not adversely affected our findings. Future work in this area could further examine the ties between political and environmental opinions. This study used human coders to detect biases from article content. While this approach was successful, it carries substantial costs which make it hard to operate for large datasets. It could be argued that our use of subjective human grading is a limitation in our analysis, but it is a necessary compromise given the inherently subjective nature of political and environmental beliefs and the current lack of objective tools for analysing such complex interpretations in large quantities of data. An automated classifier for these biases would require significant work for its creation (using a blend of machine learning and natural lan- guage processing), but would support future large-scale studies, a necessary step given the 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 21 / 25

ever-increasing volume of online content. Another research question concerns whether the patterns observed on Twitter extend to other online platforms. We studied Twitter due to its prominent role as a means for users to find news stories [55] and its frequent usage as a plat- form for lively political debates in which strangers can interact. If suitable datasets could be obtained, it would be possible to apply similar methods to other popular sites such as Facebook and Reddit; however, privacy restrictions prevent research on many social media platforms. Finally, one further question that is not addressed here is downstream exposure to shared con- tent, that is, who sees the articles that are shared by Twitter users? It is probable that the users captured in our dataset, that select and share online articles about climate change through Twitter, act as ‘opinion leaders’ (following the long-established two-step flow model of Katz and Lazarsfeld [56]), locating new information about the topic and disseminating it to their followers. Measuring the volume of downstream views and retweet rates would answer impor- tant questions about which messages are more effective when shared on social media. One key question is the extent to which polarisation exists in this secondary consumption of climate change media, or whether network effects mitigate polarisation by capturing views from both sides. Any gathering of data from online social networks requires careful consideration of the biases that it may introduce. The users of online platforms are a different distribution of people

than the general population: they tend to be younger, more wealthy and better educated [57]. Our sampling on keywords means that we will capture users who are more engaged with the topic of climate change, and moreover are significantly invested enough to share information with their followers. As such we are considering a highly-motivated sample of a certain part of society, but this is what will be visible to many users when they visit Twitter. This sampling also explains the difference from Global Warming’s Six Americas [1] that we observe, as only the “alarmed” and “dismissive” types are likely to be invested enough to be captured. The work of Williams et al. [18] support this as their coding of Twitter users found only the extreme environmentalists and sceptics. Our keywords were chosen to capture as much of the English language components of the climate change conversation online, but the observed structures may vary in other languages. These factors should be considered in any analysis of social media data but we do not believe that they affect the strength of our findings in any way. This work demonstrates that media communication of information around climate change faces many challenges in the age of social media. Users return to the same trusted sources for information even when presented with new contexts, and any attempts to attract new reader- ships need to consider this behaviour. Understanding how these trusted ties form will be key to combating the spread of misinformation that currently challenges online social networks

and vital for promoting action on climate change and other societal challenges. Supporting information S1 File. (PDF) Author Contributions Conceptualization: Tristan J. B. Cann, Iain S. Weaver. Data curation: Tristan J. B. Cann, Iain S. Weaver. Formal analysis: Tristan J. B. Cann, Iain S. Weaver, Hywel T. P. Williams. Funding acquisition: Hywel T. P. Williams. 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 22 / 25

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