Pro/Anti-vaxxers in Brazil: a temporal analysis of COVID vaccination stance in Twitter
Resumo
Collective imunization is critical to combat COVID, but a large portion of the population in many countries refuses to be vaccinated despite the availability of vaccines. We developed a temporal analysis of pro/against stances towards COVID vaccination in Brazil using Twitter. 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 anti-vaxxers were prevalent throughout 2020, expressing concerns about mandatory vaccination with a strong political bias. The pro-vaxxer movement significantly increased by late 2020 with the begging of immunization and became prevalent in 2021. This group expresses joy and anxiety to get vaccinated and criticisms towards the Federal Government.
Palavras-chave:
COVID, vaccination, dynamic topic modeling, BERTopic, Twitter, social media
Referências
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Blei, D. M., Ng, A. Y., and Jordan, M. I. Latent dirichlet allocation. J. Mach. Learn. Res. vol. 3, pp. 993–1022, 2003.
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.
Curiel, R. P. and Ramírez, H. G. Vaccination strategies against COVID-19 and the diffusion of anti-vaccination views. Scientific Reports 11 (1): 6626, 2021.
Debus, M. and Tosun, J. Political ideology and vaccination willingness: implications for policy design. Policy sciences, 2021.
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., 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.
Fridman, A., Gershon, R., and Gneezy, A. COVID-19 and vaccine hesitancy: A longitudinal study. PLoS ONE 14 (4): e0250123, 2021.
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.
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 El-Mohandes, A. A global survey of potential acceptance of a COVID-19 vaccine. Nature Medicine. 10.1038/s41591-020-1124-9 , 2020.
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.
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.
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.
Blei, D. M., Ng, A. Y., and Jordan, M. I. Latent dirichlet allocation. J. Mach. Learn. Res. vol. 3, pp. 993–1022, 2003.
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.
Curiel, R. P. and Ramírez, H. G. Vaccination strategies against COVID-19 and the diffusion of anti-vaccination views. Scientific Reports 11 (1): 6626, 2021.
Debus, M. and Tosun, J. Political ideology and vaccination willingness: implications for policy design. Policy sciences, 2021.
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., 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.
Fridman, A., Gershon, R., and Gneezy, A. COVID-19 and vaccine hesitancy: A longitudinal study. PLoS ONE 14 (4): e0250123, 2021.
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.
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 El-Mohandes, A. A global survey of potential acceptance of a COVID-19 vaccine. Nature Medicine. 10.1038/s41591-020-1124-9 , 2020.
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.
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.
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.
Publicado
04/10/2021
Como Citar
DE SOUSA, Andre Mediote; BECKER, Karin.
Pro/Anti-vaxxers in Brazil: a temporal analysis of COVID vaccination stance in Twitter. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 9. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 105-112.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2021.17467.
