Covid-19 e Tweets no Brasil: coleta, tratamento e análise de textos com evidências de estados afetivos alterados em momentos impactantes
Resumo
O Twitter recebe diariamente muitos textos (tweets) compartilhados durante eventos relevantes como desastres naturais, ocorrências de acidentes, e surtos de doenças, permitindo a pesquisadores de diversas áreas estudar e prever as relações existentes entre pessoas, locais, e até mesmo as características do espalhamento de doenças em cidades, estados, e países. Estes tweets, quando relacionados à pandemia do Novo Coronavírus (Covid-19) no Brasil, podem trazer intuições de tendência e comportamentos em momentos relevantes deste evento. Visando identificar e analisar estes momentos, coletamos, tratamos e selecionamos tweets ao longo de 90 dias, nos primeiros meses do ano de 2020, para explorar tendências de sentimentos compartilhadas por pessoas, durante a epidemia de Covid-19 no Brasil. Nestes tweets, encontramos evidências de mudança dos estados afetivos analisados em momentos relevantes, como o primeiro caso confirmado e a primeira morte no Brasil pelo Covid-19, observando altas negativas e positivas antes e a estabilização desses sentimentos após momentos de grande impacto.
Palavras-chave:
tweets, coronavírus, sentimentos
Referências
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Lerman, K., Marin, L. G., Arora, M., Lima, L. H. C., Ferrara, E., and Garcia, D. (2018). Language, demographics, emotions, and the structure of online social networks. Journal of Computational Social Science, 1(1):209–225.
Lopreite, M., Panzarasa, P., Puliga, M., and Riccaboni, M. (2021). Early warnings of covid-19 outbreaks across europe from social media. Scientific Reports, 11(2147).
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Plunz, R. A., Zhou, Y., Vintimilla, M. I. C., Mckeown, K., Yu, T., Uguccioni, L., andSutto, M. P. (2019). Twitter sentiment in New York City parks as measure of well-being. Landscape and urban planning, 189:235–246.
Qazi, U., Imran, M., and Ofli, F. (2020). Geocov19: A dataset of hundreds of millions of multilingual covid-19 tweets with location information. ACM SIGSPATIAL Special,12(1):6–15.
Ribeiro, F. N., Aráujo, M., Gonçalves, P., Gonçalves, M. A., and Benevenuto, F.(2016). Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(1):1–29.
Rodrigues de Andrade, F. M., Barreto, T. B., Herrera-Feligreras, A., Ugolini, A., and Lu,Y.-T. (2021). Twitter in Brazil: Discourses on china in times of coronavirus. Social Sciences Humanities Open, 3(1):100118.
Venerandi, A., Quattrone, G., Capra, L., Quercia, D., and Saez-Trumper, D. (2015). Measuring urban deprivation from user generated content. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing, pages 254–264.
Xiang, Y.-T., Li, W., Zhang, Q., Jin, Y., Rao, W.-W., Zeng, L.-N., Lok, G. K. I., Chow,I. H. I., Cheung, T., and Hall, B. J. (2020). Timely research papers about covid-19 in China. The Lancet (British edition), 395(10225):684–685.
Chen, E., Lerman, K., and Ferrara, E. (2020). Tracking social media discourse about thecovid-19 pandemic: Development of a public coronavirus twitter data set. JMIR Public Health Surveill, 6(2):e19273.
de Melo, T. and Figueiredo, C. M. S. (2021). Comparing news articles and tweets about covid-19 in Brazil: Sentiment analysis and topic modeling approach. JMIR PublicHealth Surveill, 7(2):e24585.
Diener, E., Lucas, R. E., and Scollon, C. N. (2006). Beyond the hedonic treadmill: revising the adaptation theory of well-being. The American psychologist, 61(4):305-314.
Farias, E. d. S. (2016). Relevância da tradução de textos de português para inglês no processo de classificação binária de sentimento de postagens rápidas em redes sociais on-line. Master’s thesis, Universidade Federal de Campina Grande.
Gallegos, L., Lerman, K., Huang, A., and Garcia, D. (2016). Geography of emotion: Where in a city are people happier? In Proceedings of the 25th International Conference Companion on World Wide Web, pages 569–574.
Györödi, C., Györödi, R., Pecherle, G., and Olah, A. (2015). A comparative study: Mongodb vs. mysql. In 13th International Conference on Engineering of Modern ElectricSystems (EMES), pages 1–6. IEEE.
Ho, V. A., Nguyen, D. H.-C., Nguyen, D. H., Thi-Van Pham, L., Nguyen, D.-V.,Van Nguyen, K., and Nguyen, N. L.-T. (2019). Emotion recognition for vietnamese social media text. In International Conference of the Pacific Association for Computa-tional Linguistics, pages 319–333. Springer.
Hutto, C. and Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1).
Jiang, J., Chen, E., Yan, S., Lerman, K., and Ferrara, E. (2020). Political polarization drives online conversations about covid-19 in the United States. Human Behavior and Emerging Technologies, 2(3):200–211.
Lerman, K., Marin, L. G., Arora, M., Lima, L. H. C., Ferrara, E., and Garcia, D. (2018). Language, demographics, emotions, and the structure of online social networks. Journal of Computational Social Science, 1(1):209–225.
Lopreite, M., Panzarasa, P., Puliga, M., and Riccaboni, M. (2021). Early warnings of covid-19 outbreaks across europe from social media. Scientific Reports, 11(2147).
Nadeau, D. and Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticæ Investigationes, 30(1):3–26.
Plunz, R. A., Zhou, Y., Vintimilla, M. I. C., Mckeown, K., Yu, T., Uguccioni, L., andSutto, M. P. (2019). Twitter sentiment in New York City parks as measure of well-being. Landscape and urban planning, 189:235–246.
Qazi, U., Imran, M., and Ofli, F. (2020). Geocov19: A dataset of hundreds of millions of multilingual covid-19 tweets with location information. ACM SIGSPATIAL Special,12(1):6–15.
Ribeiro, F. N., Aráujo, M., Gonçalves, P., Gonçalves, M. A., and Benevenuto, F.(2016). Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(1):1–29.
Rodrigues de Andrade, F. M., Barreto, T. B., Herrera-Feligreras, A., Ugolini, A., and Lu,Y.-T. (2021). Twitter in Brazil: Discourses on china in times of coronavirus. Social Sciences Humanities Open, 3(1):100118.
Venerandi, A., Quattrone, G., Capra, L., Quercia, D., and Saez-Trumper, D. (2015). Measuring urban deprivation from user generated content. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing, pages 254–264.
Xiang, Y.-T., Li, W., Zhang, Q., Jin, Y., Rao, W.-W., Zeng, L.-N., Lok, G. K. I., Chow,I. H. I., Cheung, T., and Hall, B. J. (2020). Timely research papers about covid-19 in China. The Lancet (British edition), 395(10225):684–685.
Publicado
18/07/2021
Como Citar
MAIA, Mario; OLIVEIRA, Erneson; GALLEGOS, Luciano.
Covid-19 e Tweets no Brasil: coleta, tratamento e análise de textos com evidências de estados afetivos alterados em momentos impactantes. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 10. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 79-90.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2021.16127.
