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Cited by 38Climate Change Communication and PerceptionSocial Media and PoliticsMisinformation and Its Impacts

Ideological biases in social sharing of online information about climate change

Open access
Cann, Tristan J. B., Weaver, Iain S., Williams, Hywel T. P.·2021·PLOS ONE
DOI 10.1371/journal.pone.0250656PMC PMC8064565
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Abstract

Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.

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  • Shared partisanship increases the likelihood of social tie formation on platforms like Twitter, reinforcing homophily.

    “Many studies demonstrated that shared partisanship increases the likelihood of social tie formation on platforms like Twitter, reinforcing homophily (e.g., [93][94][95]).”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [94]

    The Introduction describes echo chambers in which users associate with people and sources that reinforce their beliefs, and the Discussion reports ideologically segregated Twitter information-sharing communities. But the study examines shared links, not whether shared partisanship increases the likelihood of forming user-to-user social ties. No page numbers are supplied in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The Louvain algorithm, Leiden algorithm, random walk modelling, greedy algorithms, latent space models, flow stability or hierarchical clustering algorithms are used in most cases of the included studies to detect communities in the bipartite graph.

    “The Louvain algorithm, 10 Leiden algorithm 11 (e.g., [99]), random walk modelling (e.g., [108, 127]), greedy algorithms (e.g., [94]), latent space models 12 (e.g., [8,100]), flow stability (e.g., [146]) or hierarchical clustering algorithms (e.g., [90]) are used in most cases of the included studies to detect communities in the bipartite graph.13”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [94]

    The Methods > Community detection passage says this study used a greedy algorithm to identify communities. But it specifies that detection was performed on a unipartite network, and Methods > Network construction explains that this network was projected from the bipartite user–URL graph. The source therefore supports the greedy-algorithm example, not detection directly in the bipartite graph. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • User leaning was also estimated by activity in specific groups, by semantics in the posts, calculation of political slant of visited websites by the user, and shared news URLs with news outlets leaning score.

    “User leaning was also estimated by activity in specific groups (e.g., [59,127,143]), by semantics in the posts (e.g., [94,99,113]), calculation of political slant of visited websites by the user [124], shared news URLs with news outlets leaning score (e.g., [8,96,129]).”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [94]

    The Methods section (‘Ideological coding of source domains’; page not supplied) says the authors scored the political leaning of domains whose URLs were shared. The Results section (‘Climate media sharing is polarised and politicised’; page not supplied) uses those scores to characterise sharing-network clusters. The paper does not estimate political leaning for individual users, so it supports the cited URL-and-outlet method only at the community level. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • From these, unipartite projections or multiple bipartite graphs are derived to study user-to-user connections or user-content dynamics.

    “From these, unipartite projections or multiple bipartite graphs are derived to study user-to-user connections or user-content dynamics [94,96].”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [94]

    Methods > Network construction describes user–URL bipartite networks and a unipartite projection used to analyse sharing patterns. But that projection connects URLs, not users, so this paper does not support the claim that it studies user-to-user connections through the projection. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Most studies examined specific groups or communities on platforms, such as climate change deniers, vaccination opponents and advocates, and Trump supporters.

    “Most studies examined specific groups or communities on platforms, such as climate change deniers (e.g., [94]), vaccination opponents and advocates (e.g., [49]), and Trump supporters (e.g., [16]).”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [94]

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Research works printed but not linked: 42

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Cited by

Sorted by citations

Authors
Cann, T. J. B., Weaver, I. S., & Williams, H. T. P.
Affiliations
University of Exeter (GB)
Corresponding author
Cann, Tristan J. B.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2021-04-23
Volume
16
Issue
4
Pages
e0250656
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0250656
PMC
PMC8064565
OpenAlex
W3152712596
Cited by
38
References
7
Citations by year
2026: 7 · 2025: 9 · 2024: 7 · 2023: 8 · 2022: 7
FWCI
6.99
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
Funders
  • Engineering and Physical Sciences Research Council (EP/M506527/1)
  • Economic and Social Research Council (ES/N012283/1)
  • Natural Environment Research Council (NE/P017436/1)
Topics
  • Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords
climate change, social media, Twitter, link sharing, political ideology, ideological polarization, climate change skepticism, environmentalism, Paris Agreement withdrawal, information exchange networks, bipartite graphs, community detection, opinion formation, media engagement
MeSH terms
Social Network Analysis, Information Dissemination, Climate Change, Social Media

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