Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll

By Emily M. Cody, Andrew J. Reagan, Lewis Mitchell, Peter Sheridan Dodds, Christopher M. Danforth. PLoS ONE, 2015

304 citations recorded by the source corpus.

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.

Subfields: Communication, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/82ebdf6ad43309f9d6138c95dbef1f05384b4684

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Citations in the corpus

Determinants and Measurement of Climate Change Risk Perception, Worry, and Concern (2017, background): “Yet, with the increasing spread and transmission of risk information on social media, new theories and methods are being developed, including “social contagion theories of risk” (Scherer & Cho, 2003), social tipping points (Kinzig et al., 2013; van der Linden, 2017), “sentiment” analyses on Twitter ”
Forecasting the onset and course of mental illness with Twitter data (2016, methodology): “The use of labMT, which has shown strong prior performance in analyzing happiness on Twitter (40, 41), is novel with respect to depression screening; ANEW and LIWC have been successfully applied in previous studies on depression and Twitter (7,8,14).”
Passive crowdsourcing of social media in environmental research: A systematic map (2019, background): “The analysis of temporal patterns also needs to account for changes in the number of users over time, whereby a general increase in social media users in recent years and changes in the popularity of individual media both play a role (Cody et al., 2015).”