Feelings towards COVID-19 Vaccination: Colombian Panorama on Twitter
DOI:
https://doi.org/10.5294/pacla.2022.25.1.4Keywords:
Sentiment analysis, COVID-19, social network, Twitter, vaccinesAbstract
This document intends to analyze the sentiments underlying COVID-19 vaccination tweets. To achieve the objective, 38,034 publications from this social network are extracted through data mining, applying Machine Learning techniques, specifically sentiment analysis and network analysis, to identify the feelings expressed by Twitter users. We also identify the most relevant Twitter accounts on vaccination issues. The results suggest that feelings about vaccines are primarily negative; fear and anger, respectively, are the most recurring emotions in Twitter opinions. Moreover, we noted that the most relevant accounts belong to the media, politicians, and influencers, classified according to their feelings toward the vaccine. Opposition to the government with feelings of anger and opposition to recognized media with joyful emotions stand out.
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References
Allahyari, M. et al. (2017). A brief survey of text mining: Classification, clustering and extraction techniques. ArXiv, 1, 1-13. http://arxiv.org/abs/1707.02919
Aps, L. et al. (2018). Eventos adversos de vacinas e as consequências da não vacinação: uma análise crítica. Revista de Saude Pública, 52, 40. https://doi.org/10.11606/S1518-8787.2018052000384
Arango Pastrana, C. A. y Osorio Andrade, C. F. (2021). Aislamiento social obligatorio: un análisis de sentimientos mediante machine learning. Suma de Negocios, 12(26), 1-13. https://doi.org/10.14349/sumneg/2021.v12.n26.a1
Ashton, J. (2021). Covid-19 and the anti-vaxxers. Journal of the Royal Society of Medicine, 114(1), 42-43. https://doi.org/10.1177/0141076820986065
Atalan, A. (2020). Is the lockdown important to prevent the Covid-9 pandemic? Effects on psychology, environment and economy-perspective. Annals of Medicine and Surgery, 56, 38-42. https://doi.org/10.1016/j.amsu.2020.06.010
Bastian, M., Heymann, S. y Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks [Conference presentation]. Third International AAAI Conference on Weblogs and Social Media, 361-362. https://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/154
Carrasco, I. R. Z. y Lozano, J. C. (2018). Grupos antivacunas: el regreso global de las enfermedades prevenibles. Revista Latinoamericana de Infectología Pediátrica, 31(1), 17-21. https://www.medigraphic.com/pdfs/infectologia/lip-2018/lip181d.pdf
Carrieri, V., Madio, L. y Principe, F. (2019). Vaccine hesitancy and (fake) news: Quasi‐experimental evidence from Italy. Health Economics, 28(11), 1377-1382. https://doi.org/10.1002/hec.3937
CNN (2020). ¿En qué va la vacuna contra el coronavirus? 8 de junio. https://cnnespanol.cnn.com/2020/06/08/en-que-va-la-vacuna-contra-el-coronavirus/
Crokidakis, N. (2020). Covid-19 spreading in Rio de Janeiro, Brazil: do the policies of social isolation really work? MedRxiv, 136, 109930. https://doi.org/10.1016/j.chaos.2020.109930
Demidova, L. y Klyueva, I. (2017). Improving the classification quality of the SVM classifier for the imbalanced datasets on the base of ideas the SMOTE algorithm. ITM Web of Conferences, 10, 02002. https://doi.org/10.1051/itmconf/20171002002
Devijver, P. A. y Kittler, J. (1982). Pattern recognition: A statistical approach. Prentice Hall International.
Dubey, A. (2021). Public sentiment analysis of Covid-19 vaccination drive in India. Jaipuria Institute of Management. https://doi.org/10.2139/ssrn.3772401
El País (2020). ¿Qué dicen los datos de agosto sobre la nueva expansión del coronavirus en España? 23 de agosto. https://elpais.com/sociedad/2020-08-22/que-dicen-los-datos-de-agosto-sobre-la-nueva-expansion-del-coronavirus-en-espana.html
European Centre for Disease Prevention and Control (2020,). Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy. ECDC, 9 de marzo. https://www.ecdc.europa.eu/en/publications-data/systematic-scoping-review-social-media-monitoring-methods-and-interventions
Fernández, J. y Baquero, H. (2019). El movimiento anti-vacunas y la anti-ciencia como amenaza para la salud pública. Salud, 51(2), 104-107. https://doi.org/10.18273/revsal.v51n2-2019002
Hussein, E., Prerna Juneja, P. y Mitra, T. (2020). Measuring misinformation in video search platforms: An audit study on YouTube. [Conference] The 23rd ACM Conference on Computer-Supported Cooperative Work and Social Computing - CSCW ’20At: Minneapolis, MN, USA. https://doi.org/10.1145/3392854
Hotez, P. (2019). America and Europe’s new normal: the return of vaccine-preventable diseases. Pediatric Research, 85(7), 912-914. https://doi.org/10.1038/s41390-019-0354-3
Jamison, A. et al. (2020). Adapting and extending a typology to identify vaccine misinformation on Twitter. American Journal of Public Health, 110(S3), S331-S339. https://doi.org/10.2105/AJPH.2020.305940
Kearney, M. W. (2016). Package ‘rtweet’. https://cran.r-project.org/web/packages/rtweet/rtweet.pdf
Kotu, V. y Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer. Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-801460-8.00013-6
Kuckertz, A. et al. (2020). Startups in times of crisis – A rapid response to the Covid-19 pandemic. Journal of Business Venturing Insights, 13, e00169. https://doi.org/10.1016/j.jbvi.2020.e00169
Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2, 627-666.
Luhn, H. P. (1957). A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development, 1(4), 309-317. https://doi.org/10.1147/rd.14.0309
Manning, C., Raghavan, P. y Schutze, H. (2008). Chapter 13: Text classification and naive bayes. Introduction to Information Retrieval, 234-265. https://doi.org/10.1017/CBO9780511809071.014
Mohammad, S. y Bravo, F. (2017). WASSA-2017 shared task on emotion intensity. 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 34-49). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-5205
Montagni, I. et al. (2021). Acceptance of a Covid-19 vaccine is associated with ability to detect fake news and health literacy. Journal of Public Health, 43(4), 695-702. https://doi.org/10.1093/pubmed/fdab028
Mukherjee, K. (2020). Covid-19 and lockdown: Insights from Mumbai. Indian Journal of Public Health, 64, S168-S171. https://doi.org/10.4103/ijph.IJPH_508_20
Murphy, J. et al. (2021). Psychological characteristics associated with Covid-19 vaccine hesitancy and resistance in Ireland and the United Kingdom. Nature Communications, 12(1), 1-16. https://doi.org/10.1038/s41467-020-20226-9
Musinguzi, G. y Asamoah, B. O. (2020). The science of social distancing and total lock down: Does it work? Whom does it benefit? Electronic Journal of General Medicine, 17(6), 17-19. https://doi.org/10.29333/ejgm/7895
Neumann-Böhme, S. et al. (2020). Once we have it, will we use it? A European survey on willingness to be vaccinated against Covid-19. European Journal of Health Economics, 21(7), 977-982. https://doi.org/10.1007/s10198-020-01208-6
OMS (2020). Draft landscape of Covid-19 candidate vaccines. 20 April 2020. https://www.who.int/blueprint/priority-diseases/key-action/novel-coronavirus-landscape-ncov.pdf
Ortiz, E. (2020). Tres de cada cuatro mexicanos aceptarían aplicación de vacuna contra covid-19. UDG TV, 1 de septiembre. http://udgtv.com/noticias/tres-de-cada-cuatro-mexicanos-aceptarian-aplicacion-de-vacuna-contra-covid-19/
Pang, B. y Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. https://doi.org/10.1561/1500000011
Paul, E., Steptoe, A. y Fancourt, D. (2021). Attitudes towards vaccines and intention to vaccinate against Covid-19: Implications for public health communications. The Lancet Regional Health-Europe, 1, 100012. https://doi.org/10.1016/j.lanepe.2020.100012
Pratama, B. T., Utami, E. y Sunyoto, A. (2019). The impact of using domain specific features on lexicon based sentiment analysis on Indonesian app review. [International Conference] Information and Communications Technology (Icoiact), 24-25 de julio. https://doi.org/10.1109/ICOIACT46704.2019.8938419
Piedrahíta-Valdés, H. et al. (2021). Vaccine hesitancy on social media: Sentiment analysis from June 2011 to April 2019. Vaccines, 9(1), 28. https://doi.org/10.3390/vaccines9010028
Salleras, L. (2018). Movimientos antivacunas: una llamada a la acción. Vacunas, 19(1), 1-3. https://doi.org/10.1016/j.vacun.2018.03.001v
Sobrino, J. C. S. (2018). Análisis de sentimientos en Twitter. Inteligencia artificial. Universidad Oberta de Catalunya.
Suárez, V. M. (2020). Vacuna frente a la covid-19: administración imperativa vs. información convincente. Pediatría Integral, 24(8), 431-434.
Sued, G. (2020). El algoritmo de YouTube y la desinformación sobre vacunas durante la pandemia de covid-19. Chasqui, 1(145), 163-180. https://doi.org/10.16921/chasqui.v1i145.4335
Teso, E., Olmedilla, M., Martínez-Torres, M. R. y Toral, S. L. (2018). Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective. Technological Forecasting and Social Change, 129, 131-142. https://doi.org/10.1016/j.techfore.2017.12.018
The New York Times (2020a). Coronavirus vaccine tracker. https://www.nytimes.com/interactive/2020/science/coronavirus-vaccine-tracker.html
The New York Times (2020b). “Allá vamos otra vez”: España vive una segunda ola de coronavirus. 31 de agosto. https://www.nytimes.com/es/2020/08/31/espanol/mundo/rebrote-espana.html
Trogen, B., Oshinsky, D. y Caplan, A. (2020). Adverse consequences of rushing a Sars-CoV-2 vaccine: Implications for public trust. JAMA, 323(24), 2460-2461. https://doi.org/10.1001/jama.2020.8917
Wolfe, R. M. (2002). Anti-vaccinationists past and present. BMJ, 325(7361), 430-432. https://doi.org/10.1136/bmj.325.7361.430
Worldometer. (2020). Countries where Covid-19 has spread. https://www.worldometers.info/coronavirus/countries-where-coronavirus-has-spread/
Zucker, J. R. et al. (2020). Consequences of undervaccination—measles outbreak, New York City, 2018-2019. New England Journal of Medicine, 382(11), 1009-1017. https://doi.org/10.1056/NEJMoa1912514
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