The Importance of Poverty in Sustainability Policies: An Approach to Understanding Online Opinion
DOI:
https://doi.org/10.26439/ciis2020.5476Keywords:
social media analytics, poverty, Sustainable Development Goals (SDGs), sustainable developmentAbstract
Twitter data related to poverty and basic income was collected for 24 days in 2019, and then was cleaned and prepared for natural language processing. A 7 % subset of the data was manually labeled for sentiment analysis in order to inform the artificial intelligence (AI), which was trained and verified on this subset. We present the results for both the 7 % verification sample and the entire database. This analysis of public opinion on poverty is situated within the Sustainable Development Goals and the support for poverty reduction policies.
Downloads
References
Andreotta, M., Nugroho, R., Hurlstone, M. J., Boschetti, F., Farrell, S., Walker, I., & Paris, C. (2019). Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behavior Research Methods, 51(4), 1766-1781. https://doi.org/10.3758/s13428-019-01202-8
Arias, M., Arratia, A., & Xuriguera, R. (2014). Forecasting with twitter data. ACM Transactions on Intelligent Systems and Technolog y, 5(1), 8:1-8:24. https://doi.org/10.1145/2542182.2542190
Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc.
Brooks, D. (2019, April 4). Opinion|Winning the War on Poverty. The New York Times. https://www.nytimes.com/2019/04/04/opinion/canada-poverty-record.html
Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020). Sentiment Analysis of COVID-19 Tweets by Deep Learning Classifiers—A study to Show how Popularity is Affecting Accuracy in Social Media. Applied Soft Computing, 97, 106754. https://doi.org/10.1016/j.asoc.2020.106754
Chen, N.-C., Drouhard, M., Kocielnik, R., Suh, J., & Aragon, C. R. (2018). Using Machine Learning to Support Qualitative Coding in Social Science: Shifting the Focus to Ambiguity. ACM Trans. Interact. Intell. Syst., 8(2), 9:1–9:20. https://doi.org/10.1145/3185515
Daityari, S. (2019, September 26). How To Perform Sentiment Analysis in Python 3 Using the Natural Language Toolkit (NLTK). DigitalOcean. https://www.digitalocean.com/community/tutorials/how-to-perform-sentiment-analysis-in-python-3-using-the-natural-language-toolkit-nltk
Earl, J. (2016). “Slacktivism” that works: “Small changes” matter. The Conversation. http://theconversation.com/slacktivism-that-works-small-changes-matter-69271
Farzindar, A., & Inkpen, D. (2015). Natural Language Processing for Social Media. Morgan & Claypool Publishers. http://gen.lib.rus.ec/book/index.php?md5=10fbd73c15d6d25d8776c08835e45040
Finn, S., & Mustafaraj, E. (2013). Learning to Discover Political Activism in the Twitterverse. KI - Künstliche Intelligenz, 27(1), 17–24. https://doi.org/10.1007/s13218-012-0227-y
Global Affairs Canada-Affaires mondiales Canada. (2017, June 8). The 2030 Agenda for Sustainable Development. GAC. https://www.international.gc.ca/world-monde/issues_development-enjeux_developpement/priorities-priorites/agenda-programme.aspx?lang=eng
Government of Canada, S. C. (2019, February 26). Low income statistics by age, sex and economic family type. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1110013501
Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., & Donaldson, L. (2013). Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online. Journal of Medical Internet Research, 15(11), e239. https://doi.org/10.2196/jmir.2721
International Telecommunication Union. (2020). Individuals Using the Internet ( Percentage of population)—Canada |Data. The World Bank. https://data.worldbank.org/indicator/IT.NET.USER.ZS?locations=CA
Keramatfar, A., & Amirkhani, H. (2019). Bibliometrics of Sentiment Analysis Literature. Journal of Information Science, 45(1), 3-15. https://doi.org/10.1177/0165551518761013
Kumar, A., & Garg, G. (2020). Systematic Literature Review on Context-Based Sentiment Analysis in Social Multimedia. Multimedia Tools and Applications, 79(21-22), 15349-15380. https://doi.org/10.1007/s11042-019-7346-5
Kumar, A., & Jaiswal, A.(2020). Systematic Literature Review of Sentiment Analysis on Twitter Uusing Soft Computing Techniques. Concurrency and Computation: Practice and Experience, 32(1), e5107. https://doi.org/10.1002/cpe.5107
McGregor, K. A., & Whicker, M. E. (2018). Natural Language Processing Approaches to Understand HPV Vaccination Sentiment. Journal of Adolescent Health, 62(2), S27-S28. https://doi.org/10.1016/j.jadohealth.2017.11.055
Neuendorf, K. A., & Kumar, A. (2016). Content Analysis. In The International Encyclopedia of Political Communication (pp. 1-10). American Cancer Society. https://doi.org/10.1002/9781118541555.wbiepc065
Nexalogy. (2020). Nexalogy. https://nexalog y.com/
Oliveira, N., Cortez, P., & Areal, N. (2017). The Impact of Microblogging Data for Stock Market Pprediction: Using Twitter to Predict Returns, Volatility, Trading Volume and Survey Sentiment Indices. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2016.12.036
Patel, J., Dubey, R., & Gupta, R. K. (2020). PMI-IR Based Sentiment Analysis Over Social Media Platform for Analysing Client Review. In S. Smys, T. Senjyu, & P. Lafata (Eds.), Second International Conference on Computer Networks and Communication Technologies (pp. 204–212). Springer International Publishing. https://doi.org/10.1007/978-3-030-37051-0_23
Reyes-Menendez, A., Saura, J. R., & Alvarez-Alonso, C. (2018). Understanding #World EnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach. International Journal of Environmental Research and Public Health, 15(11), 2537. https://doi.org/10.3390/ijerph15112537
Sanz-Hernández, A. (2019a). Medios de comunicación y stakeholders: Contribución al debate público de la pobreza y justicia energética en España/Media and Stakeholders: Contribution to the Public Debate on Poverty and Energy Justice in Spain. Revista Española de Investigaciones Sociológicas, 168. https://doi.org/10.5477/cis/reis.168.73
Sanz-Hernández, A. (2019b). Social Engagement and Socio-Genesis of Energy Poverty as a Problem in Spain. Energy Policy, 124, 286-296. https://doi.org/10.1016/j.enpol.2018.10.001
Slater, M. (2018). By the numbers: Twitter Canada at Dx3 2018. Blog Twitter. https://blog.twitter.com/en_ca/topics/insights/2018/TwitterCanada_at_Dx3.html
Tuarob, S., Tucker, C. S., Salathe, M., & Ram, N. (2014). An Ensemble Heterogeneous Classification Methodology for Discovering Health-Related Knowledge in Social Media Messages. Journal of Biomedical Informatics, 49, 255–268. https://doi.org/10.1016/j.jbi.2014.03.005
United Nations. (2019). The Sustainable Development Goals Report 2019. https://www.unilibrary.org/content/publication/55eb9109-en
United Nations Statistics Division. (2019, December 20). SDG Indicators. Sustainable Development Goal Indicators Website. https://unstats.un.org/sdgs/indicators/database
