Analysis of the Twitter discourse on sustainability using natural language processing

• Autorzy: Tímea Emese Tóth
• Języki publikacji: EN

Abstrakty

EN

This publication aims to map the environmental sustainability discourse on Twitter. This will be achieved through two commonly used methods of natural language processing; topic modelling, which is used to uncover hidden themes in the document collection, and sentiment analysis, which is used to detect the attitudes of the authors of the text towards a particular attitude. The exploration of communication can provide an opportunity to find a solution to a multifaceted problem in order to protect our common future.(original abstract)

Słowa kluczowe

EN

Sustainable development; Social media; Natural languages

PL

Rozwój zrównoważony; Media społecznościowe; Języki naturalne

• Czasopismo: Edukacja Ekonomistów i Menedżerów : problemy, innowacje, projekty
• Rocznik: 2021
• Tom: 62
• Numer: nr 4
• Strony: 101--118

Twórcy

autor

Tímea Emese Tóth

• Department of Social Research Methodology, Faculty of Social Sciences, Eötvös Loránd University

Bibliografia

• Attenborough, D., & Hughes, J. (2020). A Life on Our Planet: My Witness Statement and a Vision for the Future. London: Ebury Publishing.
• Alexandratos, N., & Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision. ESA Working paper No. 12-03. Rome, FAO. Retrieved from: http:// www.fao.org/3/ap106e/ap106e.pdf (accessed: 29.03.2021).
• Ballestar, M. T., Cuerdo-Mir, M., & Freire-Rubio, M. T. (2020). The Concept of Sustainability on Social Media: A Social Listening Approach. "Sustainability", 12 (5), 2122. MDPI AG. http://dx.doi.org/10.3390/su12052122
• Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. "Journal of Machine Learning Research", 3, 993-1022.
• Cody, E. M., Reagan, A. J., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2015). Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll. "PLoS ONE", 10 (8), e0136092. DOI:10.1371/journal.pone.0136092
• Dahal, B., Kumar, S. A. P., & Li, Z. (2019). Topic modelling and sentiment analysis of global climate change tweets, "Social Network Analysis and Mining", 9 (24) https://doi.org/10.1007/ s13278-019-0568-8
• Dasgupta, P., & McKenzie, E. (2020). The Dasgupta Review - Independent Review on the Economics of Biodiversity Interim Report. Retrieved from: https://assets.publishing. service.gov.uk/government/uploads/system/uploads/attachment_data/file/882222/ The_Economics_of_Biodiversity_The_Dasgupta_Review_Interim_Report.pdf (accessed: 28.01.2021).
• Denny, M., & Spirling, A. (2018). Text Preprocessing or Unsupervised Learning: Why It Matters, When It Misleads, And What to Do About It, "Political Analysis", 26 (2), 168-189. DOI:10.1017/pan.2017.44
• Drews, S., & Miklós, A. (2016). Degrowth: A "Missile Word" That Backfires? "Ecological Economics", 126, 182-187. DOI: 10.1016/j.ecolecon.2016.04.001
• Drews, S., & Reese, G. (2018). "Degrowth" vs. Other Types of Growth: Labelling Affects Emotions but Not Attitudes". "Environmental Communication", 12 (6), 763-772. DOI: 10.1080/17524032.2018.1472127
• Evans, J. A., & Aceves, P. (2016). Machine translation: mining text for social theory. "Annual Review of Sociology", 42 (1), 21-50. https://doi.org/10.1146/annurev-soc-081715-074206
• FABLE (2020). Pathways to Sustainable Land-Use and Food Systems. 2020 Report of the FABLE Consortium. Laxenburg and Paris: International Institute for Applied Systems Analysis (IIASA) and Sustainable Development Solutions Network (SDSN). https:// doi.org/10.22022/ESM/12-2020.16896
• Furman, E., Häyhä, T., & Hirvilammi, T. (2018) A future the planet can accommodate. SKYKE & SITRA, 2. ISBN: 978-952-11-4938-2. Retrieved from: https://helda.helsinki.fi/bitstream/handle/10138/235418/PB_A-future-the-planet-can-accommodate.pdf?sequence=1 (accessed: 08.03.2021).
• Hutto, C. J., & Gilbert, E. (2015). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014.
• Intergovernmental Panel on Climate Change - The Fifth and the Sixth Assessment Cycle (2020). Retrieved from: https://www.ipcc.ch/site/assets/uploads/2020/05/2020-AC6_en.pdf (accessed: 06.03.2021).
• Kirilenko, A. P., & Stepchenkova, S. O. (2014). Public microblogging on climate change: One Year off Twitter worldwide. "Global Environmental Change", https://dx.doi.org/10.1016/j. gloenvcha.2014.02.008
• Németh, R., Katona, E. R., & Kmetty, Z. (2020). Az automatizált szövegelemzés perspektívája a társadalomtudományokban [Perspectives on automated text analysis in the social sciences]. "SZOCIOLÓGIAI SZEMLE", 30 (1), 44-62. ISSN 1216-2051.
• Nik-Bakht, M., & El-Diraby, T. E. (2016). Sus-tweet-ability: Exposing public communities perspective on sustainability of urban infrastructure through online social media. "International Journal of Human-Computer Studies", 89, 54-72, ISSN 1071-5819. https:// doi.org/10.1016/j.ijhcs.2015.11.002
• Radi, S. A., & Shokouhyar, S. (2021). Toward consumer perception of cellphones sustainability: A social media analytics. "Sustainable Production and Consumption", 25,217-233, ISSN 2352-5509. https://doi.org/10.1016/j.spc.2020.08.012
• Raworth, K. (2017). Doughnut economics: seven ways to think like a 21st-century economist. London: Random House.
• Rockström, J., & Klum, M. (2015). Big world, small planet: Abundance within planetary boundaries. USA: Yale University Press.
• Stevens, K., Kegelmeyer, P., Andrzejewski, D., & Buttler, D. (2012). Exploring topic coherence over many models and many topics. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL '12). Association for Computational Linguistics, USA, 952-961.
• https://www.consilium.europa.eu/en/policies/climate-change/paris-agreement/
• https://datareportal.com/reports/digital-2020 -global-digital-overview
• https://www.iea.org/news/after-steep-drop-in-early-2020-global-carbon-dioxide- emissions-https://www.internetlivestats.com/twitter-statistics/
• https://www.ipcc.ch/reports/
• https://ourworldindata.org/future-emissions
• https://www.overshootday.org/
• https://www.pnas.org/content/early/2016/03/16/1523119113
• https://www.protectedplanet.net/en/thematic-areas/marine-protected-areas
• https://pypi.org/project/gensim/have-rebounded-strongly Research/Programmes/Green-Cities/Urban-food-systems.htm
• https://www.rethinkx.com/food-and-agriculture-executive-summary
• https://sdg-tracker.org/
• https://www.thebluecarboninitiative.org/
• https://www.un-redd.org/
• https://www.wur.nl/en/Research-Results/Research-Institutes/Environmental-

Identyfikatory

DOI

https://doi.org/10.33119/EEIM.2021.62.5