Climate change sentiment on Twitter: An unsolicited public opinion poll
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Abstract (arXiv)
The consequences of anthropogenic climate change are extensively debated through scientific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a forum where individuals of diverse backgrounds can share their thoughts and opinions. As consumption shifts from old media to new, Twitter has become a valuable resource for analyzing current events and headline news. In this research, we analyze tweets containing the word "climate" collected between September 2008 and July 2014. Through use of a previously developed sentiment measurement tool called the Hedonometer, we determine how collective sentiment varies in response to climate change news, events, and natural disasters. We find that natural disasters, climate bills, and oil-drilling can contribute to a decrease in happiness while climate rallies, a book release, and a green ideas contest can contribute to an increase in happiness. Words uncovered by our analysis suggest that responses to climate change news are predominately from climate change activists rather than climate change deniers, indicating that Twitter is a valuable resource for the spread of climate change awareness.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2015.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 1; 2025: 1; 2026: 1.
9 papers.
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- 2023Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial riskscited by 3
- 2025ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queriescited by 3
- 2023Short text classification with machine learning in the social sciences: The case of climate change on Twittercited by 1
- 2023Curating corpora with classifiers: A case study of clean energy sentiment onlinecited by 0
- 2023Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Datacited by 0
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- 2026From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Mediacited by 0
- 2026From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media23 Apr 2026
- 2025ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries12 Jun 2025
- 2024Identifying Climate Targets in National Laws and Policies using Machine Learning3 Apr 2024
- 2023Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data5 Dec 2023
- 2023Short text classification with machine learning in the social sciences: The case of climate change on Twitter3 Oct 2023
- 2023Curating corpora with classifiers: A case study of clean energy sentiment online4 May 2023
- 2023Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks21 Mar 2023
- 2022A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets7 Nov 2022
- 2021ClimateBert: A Pretrained Language Model for Climate-Related Text22 Oct 2021