Um Framework para análise de comportamento de grupos baseado na polarização política aplicado ao contexto da COVID-19
Resumo
Mundialmente o viés político tem influenciado discussões e posicionamentos, como ocorreu na pandemia. Propusemos um framework de análise da influência da política no comportamento de grupos no Twitter, o qual agrega múltiplas dimensões: a) inferência da polarização política; b) identificação de preocupações e argumentos; c) estrutura da rede social dos grupos; d) aspectos psicológicos; e) fontes de informação. Sua aplicação em dois estudos de caso no contexto da COVID-19 mostra sua habilidade em ligar polarização política aos posicionamentos e observar seus efeitos. Esta dissertação foi aprovada com louvor e resultou em duas publicações em periódicos, duas em eventos internacionais, e uma em evento nacional premiado como melhor artigo.
Palavras-chave:
Framework de análise, Polarização política, Comportamento de grupos, Modelagem de tópicos, Análise de redes sociais, COVID-19
Referências
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Bruine de Bruin, W., Saw, H.-W., and Goldman, D. P. (2020). Political polarization in us residents’ covid-19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and Uncertainty, 61(2):177 – 194.
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De Choudhury, M., Jhaver, S., Sugar, B., and Weber, I. (2016). Social media participation in an activist movement for racial equality. In Proc. of the 10th Intl. Conf. on Web and Social Media (ICWSM), pages 92–101.
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Ebeling, R. (2021). Um framework para análise de comportamento de grupos baseado an polarização política aplicado ao contexto da covid-19. Master’s thesis, Programa de Pós-Graduação em Computação - Instituto de Informática. UFRGS.
Ebeling, R., Córdova Sáenz, C. A., 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).
Ebeling, R., Nobre, J., and Becker, K. (2023). A multi-dimensional framework to analyze group behavior based on political polarization. Expert Systems with Applications (aceito para publicacao em maio 2023 - em produção).
Ebeling, R., Sáenz, C., Nobre, J., and Becker, K. (2020a). 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, pages 89–96, Porto Alegre, RS, Brasil. SBC.
Ebeling, R., Sáenz, C., Nobre, J., and Becker, K. (2022). Analysis of the influence of political polarization in the vaccination stance: the brazilian covid-19 scenario. Proceedings of the International AAAI Conference on Web and Social Media, 16.
Ebeling, R., Sáenz, C. A. C., Nobre, J., and Becker, K. (2020b). Quarenteners vs. chloroquiners: A framework to analyze how political polarization affects the behavior of groups. In Proc. of the 2020 Conf. on Web Intelligence Conference (WI-IAT).
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.
Garimella, V. and Weber, I. (2017). A long-term analysis of polarization on twitter. In Proc. of the 11th Intl. Conf. on Web and Social Media (ICWSM), pages 528–531.
Gennaro, G. and Ash, E. (2021). Emotion and Reason in Political Language. The Economic Journal, 132(643):1037–1059.
Gerbaudo, P., Marogna, F., and Alzetta, C. (2019). When “positive posting” attracts voters: User engagement and emotions in the 2017 uk election campaign on facebook. Social Media + Society, 5(4):2056305119881695.
Grootendorst, M. (2020). Bertopic: Leveraging bert and c-tf-idf to create easily interpretable topics.
Havey, N. F. (2020). Partisan public health: how does political ideology influence support for COVID-19 related misinformation? Journal of Comp. Social Science, 3(2):319–342.
Hornsey, M. J., H. E. A. . F. K. S. (2018). The psychological roots of anti-vaccination attitudes: A 24-nation investigation. Health Psychology, 37(4):307–315.
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.
Lelkes, Y. (2016). Mass Polarization: Manifestations and Measurements. Public Opinion Quarterly, 80(S1):392–410.
Makridis, C. and Rothwell, J. T. (2020). The real cost of political polarization: Evidence from the covid-19 pandemic. Covid Economics, (34):50–87.
Ordun, C., Purushotham, S., and Raff, E. (2020). Exploratory analysis of covid-19 tweets using topic modeling, umap, and digraphs. arxiv:2005.03082.
Pennycook, G., McPhetres, J., Bago, B., and Rand, D. (2020). Predictors of attitudes and misperceptions about covid-19 in canada, the UK, and the USA. doi:10.31234/osf.io/zhjkp.
Rao, A., Morstatter, F., Hu, M., Chen, E., Burghardt, K., Ferrara, E., and Lerman, K. (2020). Political partisanship and anti-science attitudes in online discussions about covid-19. CoRR, abs/2011.08498.
Sha, H., Hasan, M. A., Mohler, G. O., and Brantingham, P. J. (2020). Dynamic topic modeling of the COVID-19 twitter narrative among U.S. governors and cabinet executives. CoRR, abs/2004.11692.
Stieglitz, S. and Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social network analysis and mining, 3(4):1277–1291.
Tausczik, Y. R. and Pennebaker, J. W. (2010). The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24–54.
Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. 3:993–1022.
Bramson, A., Grim, P., Singer, D. J., Fisher, S., Berger, W., Sack, G., and Flocken, C. (2016). Disambiguation of social polarization concepts and measures. The Journal of Mathematical Sociology, 40(2):80–111.
Bruine de Bruin, W., Saw, H.-W., and Goldman, D. P. (2020). Political polarization in us residents’ covid-19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and Uncertainty, 61(2):177 – 194.
Burki, T. (2020). The online anti-vaccine movement in the age of covid-19. The Lancet Digital Health, 2(10):e504 – e505.
Curiel, R. P. and Ramírez, H. G. (2020). Vaccination strategies against covid-19 and the diffusion of anti-vaccination views. arXiv. 2009.13674.
De Choudhury, M., Jhaver, S., Sugar, B., and Weber, I. (2016). Social media participation in an activist movement for racial equality. In Proc. of the 10th Intl. Conf. on Web and Social Media (ICWSM), pages 92–101.
Demszky, D., Garg, N., Voigt, R., Zou, J., Shapiro, J., Gentzkow, M., and Jurafsky, D. (2019). Analyzing polarization in social media: Method and application to tweets on 21 mass shootings. In Proc. of the 2019 Conf. of the North American Chapter of the ACL: Human Language Technologies, pages 2970–3005.
Ebeling, R. (2021). Um framework para análise de comportamento de grupos baseado an polarização política aplicado ao contexto da covid-19. Master’s thesis, Programa de Pós-Graduação em Computação - Instituto de Informática. UFRGS.
Ebeling, R., Córdova Sáenz, C. A., 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).
Ebeling, R., Nobre, J., and Becker, K. (2023). A multi-dimensional framework to analyze group behavior based on political polarization. Expert Systems with Applications (aceito para publicacao em maio 2023 - em produção).
Ebeling, R., Sáenz, C., Nobre, J., and Becker, K. (2020a). 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, pages 89–96, Porto Alegre, RS, Brasil. SBC.
Ebeling, R., Sáenz, C., Nobre, J., and Becker, K. (2022). Analysis of the influence of political polarization in the vaccination stance: the brazilian covid-19 scenario. Proceedings of the International AAAI Conference on Web and Social Media, 16.
Ebeling, R., Sáenz, C. A. C., Nobre, J., and Becker, K. (2020b). Quarenteners vs. chloroquiners: A framework to analyze how political polarization affects the behavior of groups. In Proc. of the 2020 Conf. on Web Intelligence Conference (WI-IAT).
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.
Garimella, V. and Weber, I. (2017). A long-term analysis of polarization on twitter. In Proc. of the 11th Intl. Conf. on Web and Social Media (ICWSM), pages 528–531.
Gennaro, G. and Ash, E. (2021). Emotion and Reason in Political Language. The Economic Journal, 132(643):1037–1059.
Gerbaudo, P., Marogna, F., and Alzetta, C. (2019). When “positive posting” attracts voters: User engagement and emotions in the 2017 uk election campaign on facebook. Social Media + Society, 5(4):2056305119881695.
Grootendorst, M. (2020). Bertopic: Leveraging bert and c-tf-idf to create easily interpretable topics.
Havey, N. F. (2020). Partisan public health: how does political ideology influence support for COVID-19 related misinformation? Journal of Comp. Social Science, 3(2):319–342.
Hornsey, M. J., H. E. A. . F. K. S. (2018). The psychological roots of anti-vaccination attitudes: A 24-nation investigation. Health Psychology, 37(4):307–315.
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.
Lelkes, Y. (2016). Mass Polarization: Manifestations and Measurements. Public Opinion Quarterly, 80(S1):392–410.
Makridis, C. and Rothwell, J. T. (2020). The real cost of political polarization: Evidence from the covid-19 pandemic. Covid Economics, (34):50–87.
Ordun, C., Purushotham, S., and Raff, E. (2020). Exploratory analysis of covid-19 tweets using topic modeling, umap, and digraphs. arxiv:2005.03082.
Pennycook, G., McPhetres, J., Bago, B., and Rand, D. (2020). Predictors of attitudes and misperceptions about covid-19 in canada, the UK, and the USA. doi:10.31234/osf.io/zhjkp.
Rao, A., Morstatter, F., Hu, M., Chen, E., Burghardt, K., Ferrara, E., and Lerman, K. (2020). Political partisanship and anti-science attitudes in online discussions about covid-19. CoRR, abs/2011.08498.
Sha, H., Hasan, M. A., Mohler, G. O., and Brantingham, P. J. (2020). Dynamic topic modeling of the COVID-19 twitter narrative among U.S. governors and cabinet executives. CoRR, abs/2004.11692.
Stieglitz, S. and Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social network analysis and mining, 3(4):1277–1291.
Tausczik, Y. R. and Pennebaker, J. W. (2010). The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24–54.
Publicado
25/09/2023
Como Citar
EBELING, Régis; BECKER, Karin.
Um Framework para análise de comportamento de grupos baseado na polarização política aplicado ao contexto da COVID-19. In: CONCURSO DE TESES E DISSERTAÇÕES (CTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 225-239.
DOI: https://doi.org/10.5753/sbbd_estendido.2023.232220.
