Treść głównego artykułu
Abstrakt
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.
Szczegóły artykułu
Autor (Autorzy) artykułu oświadcza, że przesłane opracowanie nie narusza praw autorskich osób trzecich. Wyraża zgodę na poddanie artykułu procedurze recenzji oraz dokonanie zmian redakcyjnych. Przenosi nieodpłatnie na Oficynę Wydawniczą SGH autorskie prawa majątkowe do utworu na polach eksploatacji wymienionych w art. 50 Ustawy z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych – pod warunkiem, że praca została zaakceptowana do publikacji i opublikowana.
Oficyna Wydawnicza SGH posiada autorskie prawa majątkowe do wszystkich treści czasopisma. Zamieszczenie tekstu artykuły w repozytorium, na stronie domowej autora lub na innej stronie jest dozwolone o ile nie wiąże się z pozyskiwaniem korzyści majątkowych, a tekst wyposażony będzie w informacje źródłowe (w tym również tytuł, rok, numer i adres internetowy czasopisma).
Osoby zainteresowane komercyjnym wykorzystaniem zawartości czasopisma proszone są o kontakt z Redakcją.
Referencje
- 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. Soc. Netw. Anal. Min., 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.
- Annu. Rev. Sociol., 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 Environ. 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.
Referencje
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. Soc. Netw. Anal. Min., 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.
Annu. Rev. Sociol., 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 Environ. 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.