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Public microblogging on climate change: One year of Twitter worldwide
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“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 [436]
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Cites
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Metadata
- Authors
- Kirilenko, A. P., & Stepchenkova, S. O.
- Affiliations
- University of North Dakota (US)
- University of Florida (US)
- Corresponding author
- Kirilenko, Andrei P.
- Venue
- Global Environmental Change
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 26
- Pages
- 171-182
- ISSN
- 0959-3780 (ISSN-L)
- OpenAlex
- W2102222275
- Cited by
- 254
- References
- 48
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 28 · 2023: 34 · 2022: 32 · 2021: 41 · 2020: 16 · 2019: 25 · 2018: 17 · 2017: 19 · 2016: 10 · 2015: 12 · 2014: 2
- FWCI
- 46.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Global Environmental Change (publishedVersion)
- Funders
- NASA
- Topics
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Social Sciences › Communication › Public Relations and Crisis Communication
- Social Sciences › Communication › Social Media and Politics
- Keywords
- climate change, global warming, Twitter, microblogging, public discourse, social media, spatiotemporal patterns, distributed sensors, news events, information sources, media authority, public perception, social network mining
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