Climate change on Twitter: Content, media ecology and information sharing behaviour
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This article presents a study of the content, use of sources and information sharing about climate change analysing over 60,000 tweets collected using a random week sample. We discuss the potential for studying Twitter as a communicative space that is rich in different types of information and presents both new challenges and opportunities. Our analysis combines automatic thematic analysis, semantic network analysis and text classification according to psychological process categories. We also consider the media ecology of tweets and the external web links that users shared. In terms of content, the network of topics uncovered presents a multidimensional discourse that accounts for complex causal links between climate change and its consequences. The media ecology analysis revealed a narrow set of sources with a major role played by traditional media and that emotionally arousing text was more likely to be shared.
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Natural language processing, network analysis, and clustering techniques can be used to analyze social media data to understand public opinions and discourse around climate change.
“Natural language processing, network analysis, and clustering techniques can also be used to analyze social media data to understand public opinions and discourse around climate change [436,812,844].”
From Tackling climate change with machine learning (2023) · cites this paper as [812]
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Tackling climate change with machine learning
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- Authors
- Veltri, G. A., & Atanasova, D.
- Affiliations
- University of Leicester (GB)
- Corresponding author
- Veltri, Giuseppe A.
- Venue
- Public Understanding of Science
- Publisher
- SAGE Publications
- Published
- 2015-11-26
- Volume
- 26
- Issue
- 6
- Pages
- 721-737
- ISSN
- 0963-6625 (ISSN-L)
- 1361-6609
- OpenAlex
- W2340766773
- Cited by
- 217
- References
- 45
- Citations by year
- 2026: 19 · 2025: 26 · 2024: 22 · 2023: 29 · 2022: 40 · 2021: 32 · 2020: 18 · 2019: 15 · 2018: 6 · 2017: 5 · 2016: 5
- FWCI
- 17.69
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Public Understanding of Science (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Social Sciences › Communication › Public Relations and Crisis Communication
- Social Sciences › Communication › Communication and COVID-19 Impact
- Keywords
- climate change communication, Twitter, media ecology, source use, emotional arousal, traditional media, hyperlinks, semantic networks
- MeSH terms
- Public Opinion, Internet, Information Dissemination, Climate Change, Social Media
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