Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the United States
pmc-PMC7808624.pdf · Phiên bản 1 · 16 trang
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
24. Silva C, Ribeiro B. The Importance of Stop Word Removal on Recall Values in Text Categorization. In: Proceedings of the International Joint Conference on Neural Networks, 2003. vol. 3; 2003. p. 1661– 1666 vol.3. 25. Bird Steven, Loper Edward and Klein Ewan. Natural Language Processing with Python. O’Reilly Media Inc. (2009). 26. Ro¨der M, Both A, Hinneburg A. Exploring the Space of Topic Coherence Measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining—WSDM’15. ACM Press; 2015. p. 399–408. Available from: http://dl.acm.org/citation.cfm?doid=2684822.2685324. 27. Breiman L. Random Forests. Machine Learning. 2001; 45(1):5–32. https://doi.org/10.1023/ A:1010933404324 28. Hamilton LC, Keim BD. Regional variation in perceptions about climate change. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2009; 29(15):2348–2352. https://doi.org/ 10.1002/joc.1930 29. Guyon Isabelle and Elisseeff Andre´ An introduction to variable and feature selection Journal of machine learning research. 2003; 3;1157–1182 30. Genuer R, Poggi JM, Tuleau-Malot C. Variable selection using Random Forests. Pattern Recognition Letters. 2010; 31(14):2225–2236. https://doi.org/10.1016/j.patrec.2010.03.014 31. Friedman JH. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics. 2001; 29(5):1189–1232. 32. Mann ME, Gleick PH. Climate change and California drought in the 21st century. Proceedings of the National Academy of Sciences. 2015; 112(13):3858–3859. https://doi.org/10.1073/pnas.1503667112
PMID: 25829537 33. Herring SC, Hoell A, Hoerling MP, Kossin JP, Schreck CJ III, Stott PA. Explaining extreme events of 2015 from a climate perspective. Bulletin of the American Meteorological Society. 2016; 97(12):S1– S145. 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 16 / 16