COVID-19 on Twitter: correlating vocabulary with the agravatation and attenuation of the pandemic in Brasil
Abstract
This study characterizes the first year of the COVID-19 pandemic in Brazil as a social phenomenon by analyzing the correlation between the aggravation/attenuation of the pandemic and the vocabulary used on Twitter in the weeks that precede these variations. Among other results, we observed that politically motivated terms and words with a negative tone are more prevalent in the weeks that precede the increase in the number of cases/deaths, while the use of terms related to media content (internet, music, television) is intensified in the weeks preceding the drop in the number of cases/deaths. Such results suggest the possibility of using the method introduced here for the analysis of social phenomena using computationally light and totally anonymized data from online social networks.
Keywords:
Covid, Twitter, Social Network, Anonymized Data, Covid Cases
References
Aiello, A. E., Renson, A., and Zivich, P. N. (2020). Social media and Internet-based disease surveillance for public health. Annual Review of Public Health, 41(1):101– 118.
Bae, Y., Ryu, P.-M., and Kim, H. (2014). Predicting the lifespan and retweet times of tweets based on multiple feature analysis. ETRI Journal, 36(3):418–428.
Brum, P. V., Teixeira, M. C., Miranda, R., Vimieiro, R., Meira Jr., W., and Pappa, G. L. (2020). A characterization of Portuguese tweets regarding the Covid-19 pandemic. In VIII Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), pages 177–184. Sociedade Brasileira de Computação (SBC).
Doan, S., Ohno-Machado, L., and Collier, N. (2012). Enhancing Twitter data analysis with simple semantic filtering: Example in tracking influenza-like illnesses. In 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology, pages 62–71.
França, T., Faria, F., Rangel, F., De Farias, C., and Oliveira, J. (2014). Big social data: Princípios sobre coleta, tratamento e análise de dados sociais. In Lóscio, B. F., Hara, C. S., and Martins, V., editors, Tópicos em Gerenciamento de Dados e Informações, pages 8–45. UFPR; PUC-PR, Curitiba.
Freire, F., Gonzaga, M., and Queiroz, B. (2019). Projeção populacional municipal com estimadores bayesianos, Brasil 2010-2030. Seguridade Social Municipais. Projeto Brasil, 3.
Gomide, J., Veloso, A., Meira Jr., W., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of Twitter. In Proc. of the 3rd Web Science Conf. (WebSci’11). ACM.
Li, C., Chen, L. J., Chen, X., Zhang, M., Pang, C. P., and Chen, H. (2020). Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Eurosurveillance, 25(10).
Linton, N. M., Kobayashi, T., Yang, Y., Hayashi, K., Akhmetzhanov, A. R., Jung, S.-m., Yuan, B., Kinoshita, R., and Nishiura, H. (2020). Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: A statistical analysis of publicly available case data. Journal of Clinical Medicine, 9(2):538.
Locatelli, M. S., Cunha, E. L. T. P., Guiginski, J., Franco, R. A. S., Bernardes, T., Alzamora, P. L., da Silva, D. V. F., Ganem, M. A. S., Santos, T. H. M., Carvalho, A.
I. R., Souza, L. M. V., Paixão, G. P. F., Chaves, E. F., dos Santos, G. B., dos Santos, R. V., de Freitas, A. C., Flores, M. G., Biezuner, R. F., Cardoso, R. L., Fonseca, R. M., Couto da Silva, A. P., and Meira Jr., W. (2022). Correlations between web searches and COVID-19 epidemiological indicators in Brazil. Brazilian Archives of Biology and Technology, 65.
Maia, M., Oliveira, E., and Gallegos, L. (2021). Covid-19 e tweets no brasil: coleta, tratamento e análise de textos com evidências de estados afetivos alterados em momentos impactantes. In Anais do X Brazilian Workshop on Social Network Analysis and Mining, pages 79–90, Porto Alegre, RS, Brasil. SBC.
Malagoli, L. G., Stancioli, J., Ferreira, C. H. G., Vasconcelos, M., Couto da Silva, A. P., and Almeida, J. M. (2021). A look into COVID-19 vaccination debate on Twitter. In 13th ACM Web Science Conference (WebSci’21), pages 225–233, New York. ACM.
Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2 edition.
Moreira, P., Fonseca, R., Alzamora, P., Franco, R. A., Guiginski, J., Cunha, E., Bernardes, T., Chagas, B., Ferregueti, K., Passos, L., Cardoso, L., Schneider, R., Pereira, W., da Silva, A. P., and Jr., W. M. (2021). Covid Data Analytics: Repositório de dados provenientes de múltiplas fontes sobre a pandemia de COVID-19 no Brasil. In Anais do III Dataset Showcase Workshop, pages 107–116, Porto Alegre, RS, Brasil. SBC.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM).
Shen, C., Chen, A., Luo, C., Zhang, J., Feng, B., and Liao, W. (2020). Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in mainland China: Observational infoveillance study. Journal of Medical Internet Research, 22(5):e19421.
Sousa, G., Garces, T., Cestari, V., Florêncio, R., Moreira, T., and Pereira, M. (2020). Mortality and survival of COVID-19. Epidemiology and Infection, 25(148):e123.
Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1):72–101.
Sultana, A., Tasnim, S., Mahbub Hossain, M., Bhattacharya, S., and Purohit, N. (2021). Digital screen time during the COVID-19 pandemic: a public health concern. F1000Research, 10(81).
Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., et al. (2020). Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet Infectious Diseases, 20:669–677.
Weng, J. and Lee, B.-S. (2011). Event detection in Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):401–408.
Zimmer, M. and Proferes, N. J. (2014). A topology of Twitter research: disciplines, methods, and ethics. Aslib Journal of Information Management, 66(3):250–261.
Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2).
Bae, Y., Ryu, P.-M., and Kim, H. (2014). Predicting the lifespan and retweet times of tweets based on multiple feature analysis. ETRI Journal, 36(3):418–428.
Brum, P. V., Teixeira, M. C., Miranda, R., Vimieiro, R., Meira Jr., W., and Pappa, G. L. (2020). A characterization of Portuguese tweets regarding the Covid-19 pandemic. In VIII Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), pages 177–184. Sociedade Brasileira de Computação (SBC).
Doan, S., Ohno-Machado, L., and Collier, N. (2012). Enhancing Twitter data analysis with simple semantic filtering: Example in tracking influenza-like illnesses. In 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology, pages 62–71.
França, T., Faria, F., Rangel, F., De Farias, C., and Oliveira, J. (2014). Big social data: Princípios sobre coleta, tratamento e análise de dados sociais. In Lóscio, B. F., Hara, C. S., and Martins, V., editors, Tópicos em Gerenciamento de Dados e Informações, pages 8–45. UFPR; PUC-PR, Curitiba.
Freire, F., Gonzaga, M., and Queiroz, B. (2019). Projeção populacional municipal com estimadores bayesianos, Brasil 2010-2030. Seguridade Social Municipais. Projeto Brasil, 3.
Gomide, J., Veloso, A., Meira Jr., W., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of Twitter. In Proc. of the 3rd Web Science Conf. (WebSci’11). ACM.
Li, C., Chen, L. J., Chen, X., Zhang, M., Pang, C. P., and Chen, H. (2020). Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Eurosurveillance, 25(10).
Linton, N. M., Kobayashi, T., Yang, Y., Hayashi, K., Akhmetzhanov, A. R., Jung, S.-m., Yuan, B., Kinoshita, R., and Nishiura, H. (2020). Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: A statistical analysis of publicly available case data. Journal of Clinical Medicine, 9(2):538.
Locatelli, M. S., Cunha, E. L. T. P., Guiginski, J., Franco, R. A. S., Bernardes, T., Alzamora, P. L., da Silva, D. V. F., Ganem, M. A. S., Santos, T. H. M., Carvalho, A.
I. R., Souza, L. M. V., Paixão, G. P. F., Chaves, E. F., dos Santos, G. B., dos Santos, R. V., de Freitas, A. C., Flores, M. G., Biezuner, R. F., Cardoso, R. L., Fonseca, R. M., Couto da Silva, A. P., and Meira Jr., W. (2022). Correlations between web searches and COVID-19 epidemiological indicators in Brazil. Brazilian Archives of Biology and Technology, 65.
Maia, M., Oliveira, E., and Gallegos, L. (2021). Covid-19 e tweets no brasil: coleta, tratamento e análise de textos com evidências de estados afetivos alterados em momentos impactantes. In Anais do X Brazilian Workshop on Social Network Analysis and Mining, pages 79–90, Porto Alegre, RS, Brasil. SBC.
Malagoli, L. G., Stancioli, J., Ferreira, C. H. G., Vasconcelos, M., Couto da Silva, A. P., and Almeida, J. M. (2021). A look into COVID-19 vaccination debate on Twitter. In 13th ACM Web Science Conference (WebSci’21), pages 225–233, New York. ACM.
Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2 edition.
Moreira, P., Fonseca, R., Alzamora, P., Franco, R. A., Guiginski, J., Cunha, E., Bernardes, T., Chagas, B., Ferregueti, K., Passos, L., Cardoso, L., Schneider, R., Pereira, W., da Silva, A. P., and Jr., W. M. (2021). Covid Data Analytics: Repositório de dados provenientes de múltiplas fontes sobre a pandemia de COVID-19 no Brasil. In Anais do III Dataset Showcase Workshop, pages 107–116, Porto Alegre, RS, Brasil. SBC.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM).
Shen, C., Chen, A., Luo, C., Zhang, J., Feng, B., and Liao, W. (2020). Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in mainland China: Observational infoveillance study. Journal of Medical Internet Research, 22(5):e19421.
Sousa, G., Garces, T., Cestari, V., Florêncio, R., Moreira, T., and Pereira, M. (2020). Mortality and survival of COVID-19. Epidemiology and Infection, 25(148):e123.
Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1):72–101.
Sultana, A., Tasnim, S., Mahbub Hossain, M., Bhattacharya, S., and Purohit, N. (2021). Digital screen time during the COVID-19 pandemic: a public health concern. F1000Research, 10(81).
Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., et al. (2020). Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet Infectious Diseases, 20:669–677.
Weng, J. and Lee, B.-S. (2011). Event detection in Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):401–408.
Zimmer, M. and Proferes, N. J. (2014). A topology of Twitter research: disciplines, methods, and ethics. Aslib Journal of Information Management, 66(3):250–261.
Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2).
Published
2022-07-31
How to Cite
ALZAMORA, Pedro Loures et al.
COVID-19 on Twitter: correlating vocabulary with the agravatation and attenuation of the pandemic in Brasil. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 11. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 157-168.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2022.223330.
