Understanding the COVID Vaccination Stances in Brazil: a Temporal Analysis using Twitter Data
DOI:
https://doi.org/10.5753/jidm.2022.2565Keywords:
vaccination stances, COVID-19, temporal analysis, group behavior, Twitter, topic modeling, BERTopicAbstract
Collective immunization is the only current solution available for combating COVID, but resistance towards vaccination have been observed in many countries. Brazil is a world-class reference on large-scale National Immunization Programs (NPI). However, the Federal Government was criticized for the delay and difficulties in developing a COVID NPI compatible with its large population. By May 2021, only 35 million Brazilians were vaccinated with at least one dose. This article developed a temporal analysis of pro/against stances towards COVID vaccination in Brazil. Considering tweets from February 2020 to May 2021, we summarized the main topics expressed by pro/anti-vaxxers using BERTopic, a dynamic topic modeling technique, and related them to events in the national scenario. The antivaxxers were more active throughout 2020, but the pro-vaxxers’ movement significantly increased by late 2020 with the begging of immunization, becoming prevalent in 2021. We conclude that anti-vaxxers reacted to isolated events (e.g., mandatory vaccination, political disputes) and do not constitute an effective campaign against vaccination. The pro-vaxxer’s stance denotes a continuous pro-vaccination advocacy effort, confirming that Brazil is among the most receptive countries regarding COVID vaccination.
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Amara, A., Hadj Taieb, M. A., and Ben Aouicha, M. Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis. Applied Intelligence 51 (5): 3052–3073, 2021.
Angelov, D. Top2vec: Distributed representations of topics. arXiv preprint arXiv:2008.09470, 2020.
Blei, D. M., Ng, A. Y., and Jordan, M. I. Latent dirichlet allocation. J. Mach. Learn. Res. vol. 3, pp. 993–1022, 2003.
Bryden, G. M., Browne, M., Rockloff, M., and Unsworth, C. The privilege paradox: Geographic areas with highest socio-economic advantage have the lowest rates of vaccination. Vaccine 37 (32): 4525–4532, 2019.
Cossard, A., De Francisci Morales, G., Kalimeri, K., Mejova, Y., Paolotti, D., and Starnini, M. Falling into the echo chamber: The italian vaccination debate on twitter. In Proc. of the Int. AAAI Conference on Web and Social Media. Vol. 14. pp. 130–140, 2020.
de Bruin, W. B., Saw, H.-W., and Goldman, D. P. Political polarization in us residents’ covid-19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and Uncertainty 61 (2): 177 – 194, 2020.
de Sousa, A. and Becker, K. Pro/anti-vaxxers in brazil: a temporal analysis of covid vaccination stance in twitter. In Anais do IX Symposium on Knowledge Discovery, Mining and Learning. SBC, Porto Alegre, RS, Brasil, pp. 105–112, 2021.
Debus, M. and Tosun, J. Political ideology and vaccination willingness: implications for policy design. Policy Sciences vol. 54, pp. 477–491, 2021.
Denny, M. J. and Spirling, A. Text preprocessing for unsupervised learning: Why it matters, when it misleads, and what to do about it. Political Analysis 26 (2): 168–189, 2018.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding, 2019.
Domingues, C. M. A. S., Maranhã, A. G. K., Teixeira, A. M., Fantinato, F. F. S., and Domingues, R. A. S. The Brazilian National Immunization Program: 46 years of achievements and challenges. Caderno de Saúde Pública vol. 36, 2020.
Dou, W., Wang, X., Ribarsky, W., and Zhou, M. Event detection in social media data. In IEEE VisWeek Workshop on Interactive Visual Text Analytics-Task Driven Analytics of Social Media Content. pp. 971–980, 2012.
Ebeling, R., Córdova, C., Nobre, J. C., and Becker, K. Analysis of the influence of political polarization in the vaccination stance: the brazilian covid-19 scenario. In Proc. of the 15th Intl. Conference on Web and Social Media (ICWSM). pp. 159–170, 2022.
Ebeling, R., Córdova Sáenz, C. A., Nobre, J., and Becker, K. The effect of political polarization on social distance stances in the brazilian covid-19 scenario. Journal of Information and Data Management 12 (1): 86–108, Aug., 2021.
Ebeling, R., Sáenz, C. C., Nobre, J. C., and Becker, K. Quarenteners vs. cloroquiners: a framework to analyze the effect of political polarization on social distance stances. In Anais do VIII Symposium on Knowledge Discovery, Mining and Learning. SBC, pp. 89–96, 2020.
Garcia, K. and Berton, L. Topic detection and sentiment analysis in twitter content related to covid-19 from brazil and the usa. Applied Soft Computing vol. 101, pp. 107057, 2021.
Grootendorst, M. Bertopic: Leveraging bert and c-tf-idf to create easily interpretable topics. vol. https://doi.org/10.5281/zenodo.4381785, 2020.
Hornsey, M. J., Harris, E. A., and Fielding, K. S. The psychological roots of anti-vaccination attitudes: A 24-nation investigation. Health Psychology 37 (4): 307–315, 2018.
Hotez, P. Covid vaccines: time to confront anti-vax aggression. Nature 592 (7856): 661, 2021.
Jiang, J., Chen, E., Yan, S., Lerman, K., and Ferrara, E. Political polarization drives online conversations about covid-19 in the united states. Human Behavior and Emerging Technologies 2 (3): 200–211, 2020.
Lazarus, J. V., Ratzan, S. C., Palayew, A., Gostin, L. O., Larson, H. J., Rabin, K., Kimball, S., and ElMohandes, A. A global survey of potential acceptance of a COVID-19 vaccine. Nature Medicine. 10.1038/s41591-020-1124-9, 2020.
Makridis, C. and Rothwell, J. T. The real cost of political polarization: Evidence from the covid-19 pandemic. Covid Economics, July, 2020.
Melton, C. A., Olusanya, O. A., Ammar, N., and Shaban-Nejad, A. Public sentiment analysis and topic modeling regarding covid-19 vaccines on the reddit social media platform: A call to action for strengthening vaccine confidence. Journal of Infection and Public Health 14 (10): 1505–1512, 2021. Special Issue on COVID-19 – Vaccine, Variants and New Waves.
Oliveira, F. B., Haque, A., Mougouei, D., Evans, S., Sichman, J. S., and Singh, M. P. Investigating the emotional response to covid-19 news on twitter: A topic modeling and emotion classification approachesc. IEEE Access vol. 10, pp. 16883–16897, 2022.
Omer, S. B., Benjamin, R. M., Brewer, N. T., Buttenheim, A. M., Callaghan, T., Caplan, A., Carpiano, R. M., Clinton, C., DiResta, R., Elharake, J. A., Flowers, L. C., Galvani, A. P., Lakshmanan, R., Maldonado, Y. A., McFadden, S. M., Mello, M. M., Opel, D. J., Reiss, D. R., Salmon, D. A., Schwartz, J. L., Sharfstein, J. M., and Hotez, P. J. Promoting COVID-19 vaccine acceptance: recommendations from the Lancet Commission on Vaccine Refusal, Acceptance, and Demand in the USA. The Lancet 398 (10317): 2186–2192, dec, 2021.
Pereira, C., Medeiros, A., and Bertholini, F. Fear of death and polarization: political consequences of the COVID-19 pandemic. Revista de Administracao Publica vol. 54, pp. 952 – 968, 08, 2020.
Röder, M., Both, A., and Hinneburg, A. Exploring the space of topic coherence measures. In Proc. of the 8th ACM international conf. on Web search and data mining. ACM, pp. 399–408, 2015.
Sha, H., Hasan, M. A., Mohler, G. O., and Brantingham, P. J. Dynamic topic modeling of the COVID-19 twitter narrative among U.S. governors and cabinet executives. CoRR vol. abs/2004.11692, 2020.
Solís Arce, J. S., Warren, S. S., Meriggi, N. F., Scacco, A., McMurry, N., Voors, M., Syunyaev, G., Malik, A. A., Aboutajdine, S., Adeojo, O., Anigo, D., Armand, A., Asad, S., Atyera, M., Augsburg, B., Awasthi, M., Ayesiga, G. E., Bancalari, A., Björkman Nyqvist, M., Borisova, E., Bosancianu, C. M., Cabra García, M. R., Cheema, A., Collins, E., Cuccaro, F., Farooqi, A. Z., Fatima, T., Fracchia, M., Galindo Soria, M. L., Guariso, A., Hasanain, A., Jaramillo, S., Kallon, S., Kamwesigye, A., Kharel, A., Kreps, S., Levine, M., Littman, R., Malik, M., Manirabaruta, G., Mfura, J. L. H., Momoh, F., Mucauque, A., Mussa, I., Nsabimana, J. A., Obara, I., Otálora, M. J., Ouédraogo, B. W., Pare, T. B., Platas, M. R., Polanco, L., Qureshi, J. A., Raheem, M., Ramakrishna, V., Rendra, I., Shah, T., Shaked, S. E., Shapiro, J. N., Svensson, J., Tariq, A., Tchibozo, A. M., Tiwana, H. A., Trivedi, B., Vernot, C., Vicente, P. C., Weissinger, L. B., Zafar, B., Zhang, B., Karlan, D., Callen, M., Teachout, M., Humphreys, M., Mobarak, A. M., and Omer, S. B. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nature Medicine 27 (8): 1385–1394, 2021.
Tao, G., Miao, Y., and Ng, S. COVID-19 topic modeling and visualization. In 24th Intl. Conf. on Information Visualisation (IV). IEEE, pp. 734–739, 2020.
Vargas-Calderón, V., Dominguez, M. S., Parra-A., N., Vinck-Posada, H., and Camargo, J. E. Using machine learning and information visualisation for discovering latent topics in twitter news. CoRR vol. abs/1910.09114, 2019.
Walter, R. and Becker, K. Caracterização e comparação de campanhas promovendo o outubro rosa e o novembro azul no twitter. In XXXIII Simpósio Brasileiro de Banco de Dados: Demos e WTDBD, SBBD 2018 Companion, Rio de Janeiro, RJ, Brazil, August 25-26, 2018, M. Holanda and J. M. Monteiro (Eds.). SBC, pp. 81–87, 2018.
WHO. Who coronavirus (covid-19) dashboard., 2021.
Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., and Zhu, T. Public discourse and sentiment during the covid 19 pandemic: Using latent dirichlet allocation for topic modeling on twitter. PLOS ONE 15 (9): 1–12, 09, 2020.
Zamani, M., Schwartz, H. A., Eichstaedt, J., Guntuku, S. C., Ganesan, A. V., Clouston, S., and Giorgi, S. Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing vol. 2020, pp. 193–198, nov, 2020.
