The effect of political polarization on social distance stances in the Brazilian COVID-19 scenario
DOI:
https://doi.org/10.5753/jidm.2021.1889Keywords:
political polarization, COVID-19, analysis framework, group behaviorAbstract
The COVID-19 pandemic changed the routine and concerns of people around the world since 2020. The alarming contagious rate and the lack of treatment or vaccine evoked different reactions to controlling and mitigating the virus's contagious. In this paper, we developed a case study on the Brazilian COVID scenario, investigating the influence of the political polarization in the pro/against stances of social isolation, represented in Twitter by two groups referred to as the Cloroquiners and Quarenteners. We analyzed these groups according to multiple dimensions: a) concerns expressed by each group and main arguments representing each stance; b) techniques to automatically infer from users political orientation, c) network analysis and community detection to characterize their behavior as a social network group and d) analysis of linguistic characteristics to identify psychological aspects. We propose combining two topic modeling techniques, LDA and BERTopics, to understand each stance's concerns in different granularity levels. Our main findings confirm that Cloroquiners are right-wing partisans, whereas Quarenteners are more related to the left-wing. Cloroquiners and Quarenteners' political polarization influences the arguments of economy and life and a stronger support/opposition to the president. As a group, the network of Cloroquiners is more closed and connected, and Quarenteners have a more diverse political engagement with a community of users polarized only with left-wing politicians and his supporters. In terms of psychological aspects, polarized groups come together on cognitive issues and negative emotions.
Downloads
References
Ajzenman, N., Cavalcanti, T., and Da Mata, D. More than Words: Leaders’ Speech and Risky Behavior During a Pandemic. Cambridge Working Papers in Economics 2034, Faculty of Economics, University of Cambridge. Apr., 2020.
AlSumait, L., Barbará, D., Gentle, J., and Domeniconi, C. Topic significance ranking of lda generative models. In Proc. of the European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp. 67–82, 2009.
Amora, P. R. P., Teixeira, E. M., Lima, M. I. V., Amaral, G. M., Cardozo, J. R. A., and Machado, J. C. An analysis of machine learning techniques to prioritize customer service through social networks. Journal of Information and Data Management 9 (2): 135–146, 2018.
Angelov, D. Top2vec: Distributed representations of topics, 2020.
Baccianella, S., Esuli, A., and Sebastiani, F. Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Proc. of the Intl. Conf. on Language Resources and Evaluation (LREC). Vol. 10. pp. 2200–2204, 2010.
Barrios, J. M. and Hochberg, Y. Risk perception through the lens of politics in the time of the covid-19 pandemic. Working Paper 27008, National Bureau of Economic Research. April, 2020.
Bazzan, A. L. C. I will be there for you: clique, character centrality, and community detection in friends. Computational and Applied Mathematics 39 (3), Jun, 2020.
Bedi, P. and Sharma, C. Community detection in social networks. WIREs Data Mining and Knowledge Discovery 6 (3): 115–135, 2016.
Blei, D. M., Ng, A. Y., and Jordan, M. I. Latent dirichlet allocation. vol. 3, pp. 993–1022, Mar., 2003.
Blondel, V., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics Theory and Experiment vol. 2008, 04, 2008.
Bruine de Bruin, W., Saw, H.-W., and Goldman, D. P. Political polarization in us residents’ covid-19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and Uncertainty 61 (2): 177 – 194, 2020.
Conover, M. D., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., and Flammini, A. Political polarization on twitter. In Proc. of the Fifth International Conference on Weblogs and Social Media, L. A. Adamic, R. Baeza-Yates, and S. Counts (Eds.). The AAAI Press, 2011.
Costa, L. d. F., Rodrigues, F. A., Travieso, G., and Villas Boas, P. R. Characterization of complex networks: A survey of measurements. Advances in Physics 56 (1): 167–242, Jan, 2007.
De Choudhury, M., Jhaver, S., Sugar, B., and Weber, I. Social media participation in an activist movement for racial equality. In Proc. of the 10th Intl. Conf. on Web and Social Media (ICWSM). pp. 92–101, 2016.
Demszky, D., Garg, N., Voigt, R., Zou, J., Shapiro, J., Gentzkow, M., and Jurafsky, D. 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 Association for Computational Ling.: Human Language Technologies. pp. 2970–3005, 2019.
Denny, M. J. and Spirling, A. Text preprocessing for unsupervised learning: Why it matters, when it misleads, and what to do about it. Political Analysis 26 (2): 168–189, 2018.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding, 2019.
Douven, I. and Meijs, W. Measuring coherence. Synthese 156 (3): 405–425, 2007.
Ebeling, R., Sáenz, C., Nobre, J., and Becker, K. 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. SBC, Porto Alegre, RS, Brasil, pp. 89–96, 2020a.
Ebeling, R., Sáenz, C. A. C., Nobre, J., and Becker, K. 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), 2020b.
ElSherief, M., Belding, E. M., and Nguyen, D. # notokay: Understanding gender-based violence in social media. In Proc. of the 11th Intl. Conf. on Web and Social Media (ICWSM). pp. 52–61, 2017.
Estévez, P. A., Tesmer, M., Perez, C. A., and Zurada, J. M. Normalized mutual information feature selection. IEEE Transactions on Neural Networks 20 (2): 189–201, 2009.
Fortunato, S. Community detection in graphs. Physics Reports 486 (3-5): 75–174, Feb, 2010.
Garimella, V. and Weber, I. A long-term analysis of polarization on twitter. In Proc. of the 11th Intl. Conf. on Web and Social Media (ICWSM). pp. 528–531, 2017.
Grootendorst, M. Bertopic: Leveraging bert and c-tf-idf to create easily interpretable topics., 2020.
Grossman, G., Kim, S., Rexer, J. M., and Thirumurthy, H. Political partisanship influences behavioral responses to governors’ recommendations for covid-19 prevention in the united states. Proceedings of the National Academy of Sciences 117 (39): 24144–24153, 2020.
Hansen, D. L., Shneiderman, B., Smith, M. A., and Himelboim, I. Chapter 3 - social network analysis: Measuring, mapping, and modeling collections of connections. In Analyzing Social Media Networks with NodeXL (Second Edition), Second Edition ed., D. L. Hansen, B. Shneiderman, M. A. Smith, and I. Himelboim (Eds.). Morgan Kaufmann, pp. 31 – 51, 2020.
Harb, J., Ebeling, R., and Becker, K. Exploring deep learning for the analysis of emotional reactions to terrorist events on twitter. Journal of Information and Data Management 10 (2): 97–115, 2019.
Harb, J. G. D., Ebeling, R., and Becker, K. A framework to analyze the emotional reactions to mass violent events on twitter and influential factors. Information Processing & Management 57 (6): 102372, 2020.
Hong, S. and Kim, S. H. Political polarization on twitter: Implications for the use of social media in digital governments. Government Information Quarterly 33 (4): 777 – 782, 2016.
Hornik, K. and Grün, B. topicmodels: An r package for fitting topic models. Journal of statistical software 40 (13): 1–30, 2011.
Jerônimo, C., Campelo, C., and de S. Baptista, C. Using open data to analyze urban mobility from social networks. Journal of Information and Data Management 8 (1): 83–99, 2017.
Jiang, J., Chen, E., Yan, S., Lerman, K., and Ferrara, E. Political polarization drives online conversations about covid-19 in the united states. Human Behavior and Emerging Technologies 2 (3): 200–211, 2020.
Le, Q. and Mikolov, T. Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32. ICML’14. JMLR.org, pp. II–1188–II–1196, 2014.
Makridis, C. and Rothwell, J. T. The real cost of political polarization: Evidence from the covid-19 pandemic. Covid Economics (34): 50–87, July, 2020.
Manning, C., Raghavan, P., and Schütze, H. Introduction to information retrieval. Natural Language Engineering 16 (1): 100–103, 2010.
McInnes, L. and Healy, J. Accelerated hierarchical density based clustering. In Data Mining Workshops (ICDMW), 2017 IEEE International Conference on. IEEE, pp. 33–42, 2017.
Mikolov, T., Chen, K., Corrado, G., and Dean, J. Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013,
Workshop Track Proceedings, Y. Bengio and Y. LeCun (Eds.), 2013.
Milosh, M., Painter, M., Van Dijcke, D., and Wright, A. Unmasking partisanship: How polarization influences public responses to collective risk. SSRN Electronic Journal, 01, 2020.
Mohammad, S. M. and Turney, P. D. Crowdsourcing a word-emotion association lexicon. Computational Intelligence 29 (3): 436–465, 2013.
Narayan, A., Berger, B., and Cho, H. Density-preserving data visualization unveils dynamic patterns of single-cell transcriptomic variability. bioRxiv , 2020.
Ordun, C., Purushotham, S., and Raff, E. Exploratory analysis of covid-19 tweets using topic modeling, umap, and digraphs. arxiv:2005.03082, 2020.
Pennebaker, J. W., Francis, M. E., and Booth, R. J. inguistic inquiry and word count: Liwc 2001, 2001.
Pennycook, G., McPhetres, J., Bago, B., and Rand, D. Predictors of attitudes and misperceptions about covid-19 in canada, the UK, and the USA,
Preoţiuc-Pietro, D., Liu, Y., Hopkins, D., and Ungar, L. Beyond binary labels: Political ideology prediction of twitter users. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, pp. 729–740, 2017.
Puerari, I., Duarte, D., Bianco, G. D., and Lima, J. F. Exploratory analysis of electronic health records using topic modeling. Journal of Information and Data Management 11 (2): 131–147, 2020.
Rao, A., Morstatter, F., Hu, M., Chen, E., Burghardt, K., Ferrara, E., and Lerman, K. Political partisanship and anti-science attitudes in online discussions about covid-19. CoRR vol. abs/2011.08498, 2020.
Röder, M., Both, A., and Hinneburg, A. Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on Web search and data mining. ACM, pp. 399–408, 2015.
Sha, H., Hasan, M. A., Mohler, G. O., and Brantingham, P. J. Dynamic topic modeling of the covid-19 twitter narrative among u.s. governors and cabinet executives. CoRR vol. abs/2004.11692, 2020.
Slatcher, R., Chung, C., Pennebaker, J., and Stone, L. Winning words: Individual differences in linguistic style among u.s. presidential and vice presidential candidates. Journal of Research in Personality 41 (1): 63 – 75, 2007.
Soares, F. B., Recuero, R., Volcan, T., Fagundes, G., and Sodré, G. Research note: Bolsonaro’s firehose: How covid-19 disinformation on whatsapp was used to fight a government political crisis in brazil. Harvard Kennedy School (HKS) Misinformation Review , 2021.
Souza, F., Nogueira, R., and Lotufo, R. BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear), 2020.
Tausczik, Y. R. and Pennebaker, J. W. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology 29 (1): 24–54, 2010.
Vargas-Calderón, V., Dominguez, M. S., Parra-A., N., Vinck-Posada, H., and Camargo, J. E. Using machine learning and information visualisation for discovering latent topics in twitter news. CoRR vol. abs/1910.09114, 2019.
Walter, R. and Becker, K. Caracterização e comparação de campanhas promovendo o outubro rosa e o novembro azul no twitter. In XXXIII Simpósio Brasileiro de Banco de Dados: Demos e WTDBD, SBBD 2018 Companion, Rio de Janeiro, RJ, Brazil, August 25-26, 2018, M. Holanda and J. M. Monteiro (Eds.). SBC, pp. 81–87, 2018.
