Uniting Politics and Pandemic: a Social Network Analysis on the COVID Parliamentary Commission of Inquiry in Brazil

  • Lucas Raniére Juvino Santos UFCG
  • Leandro Balby Marinho UFCG
  • Claudio Elizio Calazans Campelo UFCG

Resumo


Installed in April 2021, the COVID-19 Parliamentary Commission of Inquiry (PCI) aimed to investigate omissions and irregularities committed by the federal government during the COVID pandemic in Brazil, which resulted in the death of more than 660,000 Brazilians and placed it among the countries with the most deaths caused by COVID-19. The investigated government was elected in 2018, in one of the most polarized elections in Brazilian history, and social media played a prominent role in this polarization. Not far from that, the PCI also generated a great popular commotion on social media networks. This paper aims to analyze the public debate related to the PCI of COVID on Twitter, identifying groups, examining their characteristics and interactions, and verifying evidence of political polarization in this social network. For this, we collected 3,397,933 tweets over a period of 26 weeks, and analyzed four distinct networks, based on different types of users interactions, to identify the main actors and verify the presence of segregated groups. In addition, we use natural language preprocessing to detect group characteristics and toxic speech. As a result, we identified three users groups, based on their use of hashtags and using a community detection technique. The group against the PCI is made up of conservatives and supporters of the government targeted by the investigations and presents the highest internal homogeneity. The other two groups, moderated users and opposed to the government, are formed by actors from the most varied political spectrum, containing users from the political left, center, and right, in addition to the main media outlets in the country. Moreover, other evidences of political polarization were found even in less segregated networks, where users from different groups interact with each other, but with the presence of toxic speech.
Palavras-chave: social media network, social network analysis, political polarization, twitter

Referências

Md Tanvir Al Amin, Charu Aggarwal, Shuochao Yao, Tarek Abdelzaher, and Lance Kaplan. 2017. Unveiling Polarization in Social Networks: A Matrix Factorization Approach. In IEEE INFOCOM 2017-IEEE Conference on Computer Communications. IEEE, 1–9. https://doi.org/10.1109/INFOCOM.2017.8056959

Loris Belcastro, Riccardo Cantini, Fabrizio Marozzo, Domenico Talia, and Paolo Trunfio. 2019. Discovering Political Polarization on Social Media: A Case Study. In 2019 15th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 182–189. https://doi.org/10.1109/SKG49510.2019.00038

Loris Belcastro, Riccardo Cantini, Fabrizio Marozzo, Domenico Talia, and Paolo Trunfio. 2020. Learning Political Polarization on Social Media Using Neural Networks. IEEE Access 8 (2020), 47177–47187. https://doi.org/10.1109/ACCESS.2020.2978950

David Blei, Andrew Ng, and Michael Jordan. 2001. Latent Dirichlet Allocation. In Advances in Neural Information Processing Systems, T. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Vol. 14. MIT Press. [link].

Michael Conover, Jacob Ratkiewicz, Matthew Francisco, Bruno Gonçalves, Filippo Menczer, and Alessandro Flammini. 2011. Political Polarization on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 5. 89–96. https://ojs.aaai.org/index.php/ICWSM/article/view/14126

Régis Ebeling, Carlos Córdova Sáenz, Jeferson Campos Nobre, and Karin Becker. 2020. Quarenteners vs. Cloroquiners: A Framework to Analyze the Effect of Political Polarization on Social Distance Stances. In VIII Symposium on Knowledge Discovery, Mining and Learning. SBC, 89–96. https://doi.org/10.5753/kdmile.2020.11963

Pedro Guerra, Wagner Meira Jr, Claire Cardie, and Robert Kleinberg. 2021. A Measure of Polarization on Social Media Networks Based on Community Boundaries. Proceedings of the International AAAI Conference on Web and Social Media 7, 1, 215–224. https://ojs.aaai.org/index.php/ICWSM/article/view/14421

L GUILLAUME. 2008. Fast Unfolding of Communities in Large Networks. Journal Statistical Mechanics: Theory and Experiment 10 (2008), P1008. https://doi.org/10.1088/1742-5468/2008/10/P10008

Mathieu Jacomy, Tommaso Venturini, Sebastien Heymann, and Mathieu Bastian. 2014. ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PloS one 9, 6 (2014), e98679. https://doi.org/10.1371/journal.pone.0098679

Jordan K Kobellarz, Alexandre R Graeml, Michelle Reddy, and Thiago H Silva. 2019. Parrot Talk: Retweeting Among Twitter Users During the 2018 Brazilian Presidential Election. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web. 221–228. https://doi.org/10.1145/3323503.3349559

Changjun Lee, Jieun Shin, and Ahreum Hong. 2018. Does Social Media Use Really Make People Politically Polarized? Direct and Indirect Effects of Social Media Use on Political Polarization in South Korea. Telematics and Informatics 35, 1 (2018), 245–254. https://doi.org/10.1016/j.tele.2017.11.005

Joao A Leite, Diego F Silva, Kalina Bontcheva, and Carolina Scarton. 2020. Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (2020), 914–924. https://aclanthology.org/2020.aacl-main.91

Asdrubal Lopez-Chau, David Valle-Cruz, Rodrigo Sandoval-Almazan, and Rodrigo Sandoval-Almazan. 2019. Analyzing Polarization Through Social Media With Artificial Intelligence: The Mexican Presidential Election in 2018. In Proceedings of the 20th Annual International Conference on Digital Government Research. 502–503. https://doi.org/10.1145/3325112.3328214

Sanna Malinen, Aki Koivula, Teo Keipi, and Ilkka Koiranen. 2018. Exploring Selective Exposure and Selective Avoidance Behavior in Social Media. In Proceedings of the 9th International Conference on Social Media and Society. 350–354. https://doi.org/10.1145/3217804.3217943

Fabrizio Marozzo and Alessandro Bessi. 2018. Analyzing Polarization of Social Media Users and News Sites During Political Campaigns. Social Network Analysis and Mining 8, 1 (2018), 1–13. https://doi.org/10.1007/s13278-017-0479-5

Antonis Matakos, Evimaria Terzi, and Panayiotis Tsaparas. 2017. Measuring and Moderating Opinion Polarization in Social Networks. Data Mining and Knowledge Discovery 31, 5 (2017), 1480–1505. https://doi.org/10.1007/s10618-017-0527-9

Mark EJ Newman. 2006. Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences 103, 23 (2006), 8577–8582. https://doi.org/10.1073/pnas.0601602103

Evelien Otte and Ronald Rousseau. 2002. Social Network Analysis: A Powerful Strategy, Also for the Information Sciences. Journal of Information Science 28, 6 (2002), 441–453. https://doi.org/10.1177/016555150202800601

Felipe Bonow Soares and Raquel Recuero. 2021. Hashtag Wars: Political Disinformation and Discursive Struggles on Twitter Conversations During the 2018 Brazilian Presidential Campaign. Social Media+ Society 7, 2 (2021), 20563051211009073.

Felipe Bonow Soares, Raquel Recuero, and Gabriela Zago. 2019. Asymmetric Polarization on Twitter and the 2018 Brazilian Presidential Elections. In Proceedings of the 10th International Conference on Social Media and Society. 67–76. https://doi.org/10.1145/3328529.3328546

Stanley Wasserman, Katherine Faust, et al. 1994. Social Network Analysis: Methods and Applications. (1994). https://doi.org/10.1017/CBO9780511815478

Muheng Yang, Xidao Wen, Yu-Ru Lin, and Lingjia Deng. 2017. Quantifying Content Polarization on Twitter. In 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC). IEEE, 299–308. https://doi.org/10.1109/CIC.2017.00047

Jianhua Yin and Jianyong Wang. 2014. A Dirichlet Multinomial Mixture Model-Based Approach for Short Text Clustering. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, New York, USA) (KDD ’14). Association for Computing Machinery, New York, NY, USA, 233–242. https://doi.org/10.1145/2623330.2623715
Publicado
07/11/2022
Como Citar

Selecione um Formato
SANTOS, Lucas Raniére Juvino; MARINHO, Leandro Balby; CAMPELO, Claudio Elizio Calazans. Uniting Politics and Pandemic: a Social Network Analysis on the COVID Parliamentary Commission of Inquiry in Brazil. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 105-113.