Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the United States

References 1. Howe PD, Mildenberger M, Marlon JR, Leiserowitz A. Geographic Variation in Opinions on Climate Change at State and Local Scales in the USA. Nature Climate Change. 2015; 5(6):596–603. https://doi. org/10.1038/nclimate2583 2. Nisbet MC. Communicating Climate Change: Why Frames Matter for Public Engagement. Environ- ment: Science and Policy for Sustainable Development. 2009; 51(2):12–23. https://doi.org/10.3200/ ENVT.51.2.12-23 3. Pearce W, Holmberg K, Hellsten I, Nerlich B. Climate Change on Twitter: Topics, Communities and Conversations about the 2013 IPCC Working Group 1 Report. PLOS ONE. 2014; 9(4):e94785. https:// doi.org/10.1371/journal.pone.0094785 PMID: 24718388 4. Cody EM, Reagan AJ, Mitchell L, Dodds PS, Danforth CM. Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll. 2015; 10(8):e0136092. https://doi.org/10.1371/journal.pone.0136092 5. Roxburgh N, Guan D, Shin KJ, Rand W, Managi S, Lovelace R, et al. Characterising Climate Change Discourse on Social Media during Extreme Weather Events. Global Environmental Change. 2019; 54:50–60. https://doi.org/10.1016/j.gloenvcha.2018.11.004 6. Milfont TL, Evans L, Sibley CG, Ries J, Cunningham A. Proximity to Coast Is Linked to Climate Change Belief. PLOS ONE. 2014; 9(7):e103180. https://doi.org/10.1371/journal.pone.0103180 PMID: 25047568 7. Kirilenko AP, Stepchenkova SO. Public Microblogging on Climate Change: One Year of Twitter World- wide. Global Environmental Change. 2014; 26:171–182. https://doi.org/10.1016/j.gloenvcha.2014.02. 008

8. Pasek J, Krosnick JA. Optimizing survey questionnaire design in political science: Insights from psy- chology. Oxford handbook of American elections and political behavior. 2010; p. 27–50. 9. Cannell CF, Miller PV, Oksenberg L. Research on interviewing techniques. Sociological methodology. 1981; 12:389–437. https://doi.org/10.2307/270748 10. Krosnick JA. Response strategies for coping with the cognitive demands of attitude measures in sur- veys. Applied cognitive psychology. 1991; 5(3):213–236. https://doi.org/10.1002/acp.2350050305 11. Turner C, Martin E. Surveying subjective phenomena. vol. 2. Russell Sage Foundation; 1985. 12. Glynn CJ, Herbst S, Lindeman M, O’Keefe GJ, Shapiro RY. Public Opinion. Routledge; 2015. Available from: http://ebookcentral.proquest.com/lib/purdue/detail.action?docID=1181617. 13. Fownes JR, Yu C, Margolin DB. Twitter and Climate Change. Sociology Compass. 2018; 12(6):e12587. https://doi.org/10.1111/soc4.12587 14. Kirilenko AP, Molodtsova T, Stepchenkova SO. People as Sensors: Mass Media and Local Tempera- ture Influence Climate Change Discussion on Twitter. Global Environmental Change. 2015; 30:92–100. https://doi.org/10.1016/j.gloenvcha.2014.11.003 15. Palen L, Anderson KM. Crisis Informatics—New Data for Extraordinary Times. Science. 2016; 353 (6296):224–225. https://doi.org/10.1126/science.aag2579 PMID: 27418492 16. Auer MR, Zhang Y, Lee P. The Potential of Microblogs for the Study of Public Perceptions of Climate Change. Wiley Interdisciplinary Reviews: Climate Change. 2014; 5(3):291–296. https://doi.org/10. 1002/wcc.273

17. Williams HTP, McMurray JR, Kurz T, Hugo Lambert F. Network Analysis Reveals Open Forums and Echo Chambers in Social Media Discussions of Climate Change. Global Environmental Change. 2015; 32:126–138. https://doi.org/10.1016/j.gloenvcha.2015.03.006 18. Jang SM, Hart PS. Polarized Frames on “Climate Change” and “Global Warming” across Countries and States: Evidence from Twitter Big Data. Global Environmental Change. 2015; 32:11–17. https://doi.org/ 10.1016/j.gloenvcha.2015.02.010 19. An X, Ganguly AR, Fang Y, Scyphers SB, Hunter AM, Dy JG. Tracking climate change opinions from twitter data. In: Workshop on Data Science for Social Good; 2014. 20. Zhao, Wayne Xin and Jiang, Jing and Weng, Jianshu and He, Jing and Lim, Ee-Peng and Yan, et al. Xiaoming Comparing Twitter and Traditional Media Using Topic Models Advances in Information Retrieval, 2011 21. Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. Journal of Machine Learning Research. 2003; 3 (Jan):993–1022. 22. Rˇ ehůřek R, Sojka P. Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Valletta, Malta: ELRA; 2010. p. 45–50. 23. McCallum AK. MALLET: A Machine Learning for Language Toolkit; 2002. PLOS ONE An approach for augmenting surveys with social media to understand climate opinion in the United States PLOS ONE | https://doi.org/10.1371/journal.pone.0245319 January 14, 2021 15 / 16