Análise do estresse e tópicos discutidos no Twitter durante a pandemia da COVID-19 no Brasil
Resumo
Este trabalho propõe um aplicação para mensurar a incidência de estresse durante a pandemia da COVID-19 por meio do algoritmo TensiStrength (TS) adaptado para o português e de técnicas de processamento de linguagem natural em tweets. Como resultado, foi possível validar o TS para mensurar o estresse e relaxamento, bem como descrever as discussões relacionadas à pandemia no Brasil por meio de diferentes algoritmos de extração de tópicos e visualização de nuvens de palavras.Referências
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Egger, R. and Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in sociology, 7.
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Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
Habibabadi, S. K. and Haghighi, P. D. (2019). Topic modelling for identification of vaccine reactions in twitter. In Proceedings of the Australasian Computer Science Week Multiconference, pages 1–10.
Li, L., Zhang, Q., Wang, X., Zhang, J., Wang, T., Gao, T.-L., Duan, W., Tsoi, K. K.-f., and Wang, F.-Y. (2020). Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on Computational Social Systems, 7(2):556–562.
Lin, H., Jia, J., Guo, Q., Xue, Y., Li, Q., Huang, J., Cai, L., and Feng, L. (2014). User-level psychological stress detection from social media using deep neural network. In Proceedings of the 22nd ACM international conference on Multimedia, pages 507–516.
Ljajić, A., Prodanović, N., Medvecki, D., Bašaragin, B., and Mitrović, J. (2022). Uncovering the reasons behind covid-19 vaccine hesitancy in serbia: Sentiment-based topic modeling. Journal of Medical Internet Research, 24(11):e42261.
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Menuzzo, V. A., Santanchè, A., and Gomes-Jr, L. (2021). Evaluating the cohesion of municipalities’ discourse during the covid-19 pandemic. In Anais do XXXVI Simpósio Brasileiro de Bancos de Dados, pages 295–300. SBC.
Plackett, R. L. (1983). Karl pearson and the chi-squared test. International statistical review/revue internationale de statistique, pages 59–72.
Taylor, S., Landry, C. A., Paluszek, M. M., Fergus, T. A., McKay, D., and Asmundson, G. J. (2020). Development and initial validation of the covid stress scales. Journal of Anxiety Disorders, 72:102232.
Thelwall, M. (2017). Tensistrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1):106–121.
Wang, X., Zhang, H., Cao, L., and Feng, L. (2020). Leverage social media for personalized stress detection. In Proceedings of the 28th ACM International Conference on Multimedia, pages 2710–2718.
Afonso, P. (2020). The impact of the covid-19 pandemic on mental health. Acta medica portuguesa, 33(5):356–357.
Alzamora, P. L., Locatelli, M. S., Ganem, M., Santos, T. H. M., Ferreira, D. V., Bernardes, T., Franco, R. A., Guiginski, J., Cunha, E. L., da Silva, A. P. C., et al. (2022). A covid-19 no twitter: correlacionando vocabulário com agravamento e atenuação da pandemia no brasil. In Anais do XI Brazilian Workshop on Social Network Analysis and Mining, pages 157–168. SBC.
Bouma, G. (2009). Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL, 30:31–40.
Brown, S. M., Doom, J. R., Lechuga-Peña, S., Watamura, S. E., and Koppels, T. (2020). Stress and parenting during the global covid-19 pandemic. Child abuse & neglect, 110:104699.
Campagnolo, J. M., Duarte, D., and Dal Bianco, G. (2022). Topic coherence metrics: How sensitive are they? Journal of Information and Data Management, 13(4).
de Sousa, A. M. 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. SBC.
de Sousa, A. M. and Becker, K. (2022). Comparando os posicionamentos a favor/contra a vacinação covid nos estados unidos da américa e no brasil. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados, pages 65–77. SBC.
Ebeling, R., Sáenz, C. A. C., Nobre, J., and Becker, K. (2021). The effect of political polarization on social distance stances in the brazilian covid-19 scenario. Journal of Information and Data Management, 12(1).
Egger, R. and Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in sociology, 7.
Freitas, D. P., Borges, M. R., and Carvalho, P. V. R. d. (2020). A conceptual framework for developing solutions that organise social media information for emergency response teams. Behaviour & Information Technology, 39(3):360–378.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
Habibabadi, S. K. and Haghighi, P. D. (2019). Topic modelling for identification of vaccine reactions in twitter. In Proceedings of the Australasian Computer Science Week Multiconference, pages 1–10.
Li, L., Zhang, Q., Wang, X., Zhang, J., Wang, T., Gao, T.-L., Duan, W., Tsoi, K. K.-f., and Wang, F.-Y. (2020). Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on Computational Social Systems, 7(2):556–562.
Lin, H., Jia, J., Guo, Q., Xue, Y., Li, Q., Huang, J., Cai, L., and Feng, L. (2014). User-level psychological stress detection from social media using deep neural network. In Proceedings of the 22nd ACM international conference on Multimedia, pages 507–516.
Ljajić, A., Prodanović, N., Medvecki, D., Bašaragin, B., and Mitrović, J. (2022). Uncovering the reasons behind covid-19 vaccine hesitancy in serbia: Sentiment-based topic modeling. Journal of Medical Internet Research, 24(11):e42261.
Martins, J. S., Lenz, M. L., and Silva, M. B. F. (2020). PROCESSAMENTOS DE LINGUAGEM NATURAL. Grupo A, Porto Alegre.
Menuzzo, V. A., Santanchè, A., and Gomes-Jr, L. (2021). Evaluating the cohesion of municipalities’ discourse during the covid-19 pandemic. In Anais do XXXVI Simpósio Brasileiro de Bancos de Dados, pages 295–300. SBC.
Plackett, R. L. (1983). Karl pearson and the chi-squared test. International statistical review/revue internationale de statistique, pages 59–72.
Taylor, S., Landry, C. A., Paluszek, M. M., Fergus, T. A., McKay, D., and Asmundson, G. J. (2020). Development and initial validation of the covid stress scales. Journal of Anxiety Disorders, 72:102232.
Thelwall, M. (2017). Tensistrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1):106–121.
Wang, X., Zhang, H., Cao, L., and Feng, L. (2020). Leverage social media for personalized stress detection. In Proceedings of the 28th ACM International Conference on Multimedia, pages 2710–2718.
Publicado
06/08/2023
Como Citar
PERES, Diansley R. S.; SILVA, Gean F. da; FARIA, Elaine R.; BARIONI, Maria Camila N..
Análise do estresse e tópicos discutidos no Twitter durante a pandemia da COVID-19 no Brasil. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 12. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 43-54.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2023.229752.
