Comparing positions for/against COVID vaccination in the United States of America and Brazil
Abstract
The World Health Organization endorses vaccination as the most effective way to fight the COVID-19 pandemic, but hesitation to get vaccinated is a reality in many countries. This article develops a temporal study of stances for/against COVID-19 vaccination in the United States of America (USA), drawing a parallel with Brazil using data from Twitter. We conclude that the anti-vaccination movement in the US is more dominant in relation to Brazil. While there are many common elements between the two countries, the US population is more objective in defending their positions (health and safety versus individual freedom). In Brazil, we observe the population's anxiety about getting vaccinated and a political bias.
Keywords:
vaccination stances, COVID-19, temporal analysis, group behavior, Twitter, topic modeling, BERTopic, USA, Brazil
References
Angelov, D. (2020). Top2vec: Distributed representations of topics. arXiv preprint arXiv:2008.09470.
Anonymous (2021). Anonymous. In Anonymous.
Cascini, F., Pantovic, A., Al-Ajlouni, Y., Failla, G., and Ricciardi, W. (2021). Attitudes, acceptance and hesitancy among the general population worldwide to receive the COVID-19 vaccines and their contributing factors: A systematic review. EClinicalMedicine, 40.
de Sousa, A. and Becker, K. (2021). 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, pages 105-112, Porto Alegre, RS, Brasil. SBC.
Ebeling, R., Cordova, C., Nobre, J. C., and Becker, K. (2022). 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).
Garcia, K. and Berton, L. (2021). Topic detection and sentiment analysis in twitter content related to covid-19 from brazil and the usa. Applied Soft Computing, 101:107057.
Grootendorst, M. (2020). Bertopic: Leveraging bert and c-tf-idf to create easily interpretable topics. https://doi.org/10.5281/zenodo.4381785.
Hornsey, M. J., Harris, E. A., and Fielding, K. S. (2018). The psychological roots of antivaccination attitudes: A 24-nation investigation. Health Psychology, 37(4):307-315.
Hotez, P. (2021). Covid vaccines: time to confront anti-vax aggression.Nature, 592(7856):661.
Huangfu, L., Mo, Y., Zhang, P., Zeng, D. D., and He, S. (2022). Covid-19 vaccine tweets after vaccine rollout: Sentiment-based topic modeling. J Med Internet Res, 24(2):e31726.
Liu, J., Nie, H., Li, S., Chen, X., Cao, H., Ren, J., Lee, I., and Xia, F. (2021). Tracing the pace of covid-19 research: Topic modeling and evolution. Big Data Research, 25:100236.
Ma, P., Zeng-Treitler, Q., and Nelson, S. J. (2020). Use of two topic modeling methods to investigate covid vaccine hesitancy. In Proc. of the 14th Intl. Conf. on ICT, Society, and Human Beings (ICT2021), volume 14, pages 130-140.
Oliveira, F. B., Haque, A., Mougouei, D., Evans, S., Sichman, J. S., and Singh, M. P. (2022). Investigating the emotional response to covid-19 news on twitter: A topic modeling and emotion classification approaches. IEEE Access, 10:16883-16897.
Omer, S. and et alli (2021). 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.
Solís Arce, J. and et alli (2021). COVID-19 vaccine acceptance and hesitancy in low-and middle-income countries. Nature Medicine, 27(8):1385-1394.
Song, K., Tan, X., Qin, T., Lu, J., and Liu, T.-Y. (2020). Mpnet: Masked and permuted pre-training for language understanding. arXiv preprint arXiv:2004.09297.
Tao, G., Miao, Y., and Ng, S. (2020). COVID-19 topic modeling and visualization. In 24th Intl. Conf. on Information Visualisation (IV), pages 734-739. IEEE.
Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., and Zhu, T. (2020). Public discourse and sentiment during the covid 19 pandemic: Using latent dirichlet allocation for topic modeling on twitter. PLOS ONE, 15(9):1-12.
Anonymous (2021). Anonymous. In Anonymous.
Cascini, F., Pantovic, A., Al-Ajlouni, Y., Failla, G., and Ricciardi, W. (2021). Attitudes, acceptance and hesitancy among the general population worldwide to receive the COVID-19 vaccines and their contributing factors: A systematic review. EClinicalMedicine, 40.
de Sousa, A. and Becker, K. (2021). 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, pages 105-112, Porto Alegre, RS, Brasil. SBC.
Ebeling, R., Cordova, C., Nobre, J. C., and Becker, K. (2022). 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).
Garcia, K. and Berton, L. (2021). Topic detection and sentiment analysis in twitter content related to covid-19 from brazil and the usa. Applied Soft Computing, 101:107057.
Grootendorst, M. (2020). Bertopic: Leveraging bert and c-tf-idf to create easily interpretable topics. https://doi.org/10.5281/zenodo.4381785.
Hornsey, M. J., Harris, E. A., and Fielding, K. S. (2018). The psychological roots of antivaccination attitudes: A 24-nation investigation. Health Psychology, 37(4):307-315.
Hotez, P. (2021). Covid vaccines: time to confront anti-vax aggression.Nature, 592(7856):661.
Huangfu, L., Mo, Y., Zhang, P., Zeng, D. D., and He, S. (2022). Covid-19 vaccine tweets after vaccine rollout: Sentiment-based topic modeling. J Med Internet Res, 24(2):e31726.
Liu, J., Nie, H., Li, S., Chen, X., Cao, H., Ren, J., Lee, I., and Xia, F. (2021). Tracing the pace of covid-19 research: Topic modeling and evolution. Big Data Research, 25:100236.
Ma, P., Zeng-Treitler, Q., and Nelson, S. J. (2020). Use of two topic modeling methods to investigate covid vaccine hesitancy. In Proc. of the 14th Intl. Conf. on ICT, Society, and Human Beings (ICT2021), volume 14, pages 130-140.
Oliveira, F. B., Haque, A., Mougouei, D., Evans, S., Sichman, J. S., and Singh, M. P. (2022). Investigating the emotional response to covid-19 news on twitter: A topic modeling and emotion classification approaches. IEEE Access, 10:16883-16897.
Omer, S. and et alli (2021). 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.
Solís Arce, J. and et alli (2021). COVID-19 vaccine acceptance and hesitancy in low-and middle-income countries. Nature Medicine, 27(8):1385-1394.
Song, K., Tan, X., Qin, T., Lu, J., and Liu, T.-Y. (2020). Mpnet: Masked and permuted pre-training for language understanding. arXiv preprint arXiv:2004.09297.
Tao, G., Miao, Y., and Ng, S. (2020). COVID-19 topic modeling and visualization. In 24th Intl. Conf. on Information Visualisation (IV), pages 734-739. IEEE.
Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., and Zhu, T. (2020). Public discourse and sentiment during the covid 19 pandemic: Using latent dirichlet allocation for topic modeling on twitter. PLOS ONE, 15(9):1-12.
Published
2022-09-19
How to Cite
SOUSA, André Mediote de; BECKER, Karin.
Comparing positions for/against COVID vaccination in the United States of America and Brazil. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 65-77.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2022.224628.
