Abstract
Political events are often topics of heated discussions around the globe, revealing opinion divergences of the population. These contrasting ideas characterize political polarization, which has been boosted by the popularization of Internet access and social media over the last few years. This work studies political polarization by developing computational methods to analyze online and offline data in the context of the 2016 impeachment proceedings of Dilma Rousseff in Brazil. We quantify the polarization among the Brazilian politicians at the House of Representatives (offline analysis) and among the Brazilian general public on Twitter (online analysis). We also looked at the popularity of politicians on Twitter and contrasted it with the polarization of the general public on this same media. Our results show that the politicians’ polarization increased after December of 2015, coinciding with the launch of the impeachment proceedings. The general public presented high values of polarization during the whole period, also revealing that the population was more polarized than its representatives. The politicians’ popularity analysis also shows that anti-impeachment politicians had a higher impact on the public opinion for the whole period of study than pro-impeachment politicians, which were popular only during a short but critical three months period.
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Twitter user ids can be made available under request. All other data sources are public and clearly indicated in the manuscript.
Notes
Data is made available by Brazil’s House of Representatives through web services: http://www.camara.leg.br/SitCamaraWS/Proposicoes.asmx/ListarProposicoesVotadasEmPlenario (proposed bills) and http://www.camara.leg.br/SitCamaraWS/Proposicoes.asmx/ObterProposicaoPorID (votes).
The complete interactive heat map can be seen on https://sites.google.com/view/robertacoeli/snam_2020/supertopics.
References
Abramowitz AI, Saunders KL (2008) Is polarization a myth? J Politics 70(2):542–555
Adamic LA, Glance N (2005) The political blogosphere and the 2004 U.S. Election: divided they blog. In: Proceedings of the 3rd international workshop on link discovery, LinkKDD ’05. ACM Press, New York, pp 36–43
Badawy A, Addawood A, Lerman K, Ferrara E (2019) Characterizing the 2016 Russian IRA influence campaign. Soc Netw Anal Min 9:31
Baldassarri D, Gelman A (2008) Partisans without constraint: political polarization and trends in American public opinion. Am J Sociol 114(2):408–446
Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd international conference on machine learning. ACM, New York, pp 113–120
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Boutet A, Kim H, Yoneki E (2013) What’s in Twitter, I know what parties are popular and who you are supporting now!. Soc Netw Anal Min 3(4):1379–1391
Bramson A, Grim P, Singer DJ, Fisher S, Berger W, Sack G, Flocken C (2016) Disambiguation of social polarization concepts and measures. J Math Sociol 40(2):80–111
Carvalho CdS, França FO, Goya DH, Penteado CLP (2016) The people have spoken: conflicting Brazilian protests on twitter. In: Proceedings of HICSS
Conover M, Ratkiewicz J, Francisco MR, Gonçalves B, Menczer F, Flammini A (2011) Political polarization on Twitter. In: Proceedings of the fifth international AAAI on Weblogs and Social Media, ICWSM ’11, vol 133. Association for the Advancement of Artificial Intelligence, Barcelona, pp 89–96
de França FO, Goya DH, de Camargo Penteado CL (2018) User profiling of the Twitter social network during the impeachment of Brazilian president. Soc Netw Anal Min 8(1):5
Das A, Gollapudi S, Munagala K (2014) Modeling opinion dynamics in social networks. In: Proceedings of the 7th ACM international conference on Web Search and Data Mining, WSDM ’14. ACM, New York, pp 403–412
Davidov D, Tsur O, Rappoport A (2010) Enhanced sentiment learning using Twitter hashtags and smileys. In: Proceedings of the 23rd international conference on computational linguistics: posters. Association for Computational Linguistics, pp 241–249
Davidson I, Gourru A, Velcin J, Wu Y (2020) Behavioral differences: insights, explanations and comparisons of French and us Twitter usage during elections. Soc Netw Anal Min 10(1):6
DiMaggio P, Evans J, Bryson B (1996) Have American’s social attitudes become more polarized? Am J Sociol 102(3):690–755
Druckman JN, Peterson E, Slothuus R (2013) How elite partisan polarization affects public opinion formation. Am Polit Sci Rev 107(1):57–79
Farrell H (2012) The consequences of the Internet for politics. Annu Rev Polit Sci 15:35–52
Farrell H, Drezner DW (2008) The power and politics of blogs. Public Choice 134(1–2):15–30
Fiorina MP, Abrams SJ (2008) Political polarization in the American public. Annu Rev Polit Sci 11(1):563–588
Garimella K, Morales GDF, Gionis A, Mathioudakis M (2018) Quantifying controversy on social media. ACM Trans Soc Comput 1(1):1–27
Guerra PHC, Meira W Jr, Cardie C, Kleinberg R (2013) A measure of polarization on social media networks based on community boundaries. In: Proceedings of the seventh international AAAI Conference on Weblogs and Social Media, ICWSM ’13. Association for the Advancement of Artificial Intelligence, pp 1–10
IBOPE (2016) Internet e Política: Ativismo nas Redes Sociais. http://www.ibopeinteligencia.com/noticias-e-pesquisas/metade-dos-eleitores-brasileiros-receberam-informacoes-sobre-politica-pelo-facebook-twitter-ou-whatsapp. Accessed 15 Nov 2018
Ileri I, Karagoz P (2016) Detecting user emotions in Twitter through collective classification. In: Proceedings of the 8th international joint conference on knowledge discovery, vol 1. Knowledge Engineering and Knowledge Management, Science and Technology Publications, pp 205–212
Jiang W, Wu J (2017) Active opinion-formation in online social networks. In: IEEE Conference on Computer Communications. IEEE, pp 1–9. https://doi.org/10.1109/INFOCOM.2017.8057103
Joseph K, Swire-Thompson B, Masuga H, Baum M, Lazer D (2019) Polarized, together: comparing partisan support For trump’s tweets using survey and platform-based measures. In: Proceedings of ICWSM
Kiritchenko S, Zhu X, Mohammad SM (2014) Sentiment analysis of short informal texts. J Artif Intell Res 50:723–762
Kouloumpis E, Wilson T, Moore J (2011) Twitter sentiment analysis: the good the bad and the omg! In: Proceedings of the fifth international AAAI Conference on Weblogs and Social Media, ICWSM ’11. AAAI, pp 538–541
Lahuerta-Otero E, Cordero-Gutiérrez R, De la Prieta-Pintado F (2018) Retweet or like? That is the question. Online Information Review
Li J, Li X, Zhu B (2016) User opinion classification in social media: a global consistency maximization approach. Inf Manag 53(8):987–996
Lietz H, Wagner C, Bleier A, Strohmaier M (2014) When politicians talk: assessing online conversational practices of political parties on twitter. In: Proceedings of the eighth international AAAI Conference on Weblogs and Social Media. AAAI, pp 285–294
Livne A, Simmons M, Adar E, Adamic L (2011) The party is over here: structure and content in the 2010 election. In: Proceedings of the fifth international AAAI Conference on Weblogs and Social Media, ICWSM ’11. AAAI, Barcelona, p SI
Makazhanov A, Rafiei D, Waqar M (2014) Predicting political preference of Twitter users. Soc Netw Anal Min 4(1):193
Mei Q, Zhai C (2005) Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, pp 198–207
Metaxas P, Mustafaraj E, Wong K, Zeng L, O’Keefe M, Finn S (2015) What do retweets indicate? Results from user survey and meta-review of research. In: Proceedings of ICWSM
Missen MMS, Boughanem M, Cabanac G (2013) Opinion mining: reviewed from word to document level. Soc Netw Anal Min 3(1):107–125
Mohammad SM (2012) #emotional tweets. In: Proceedings of the first joint conference on lexical and computational semantics, SemEval ’12. Association for Computational Linguistics, Stroudsburg, pp 246–255
Mohammad SM, Sobhani P, Kiritchenko S (2017) Stance and sentiment in tweets. ACM Trans Internet Technol 17(3):1–23. https://doi.org/10.1145/3003433
Morales AJ, Borondo J, Losada JC, Benito RM (2015) Measuring political polarization: Twitter shows the two sides of Venezuela. Chaos Interdiscip J Nonlinear Sci 25(3):033114
Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582
O’Connor B, Balasubramanyan R, Routledge BR, Smith NA, et al. (2010) From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the fourth international AAAI Conference on Weblogs and Social Media. AAAI, pp 122–129
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retrieval 2(1–2):1–135
Rabelo JCB, Prudencio RBC, Barros FA (2012) Using link structure to infer opinions in social networks. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 681–685
Ribeiro VA, Gomes Goveia F (2016) A Comissão do Impeachment na Rede: o Histórico das Narrativas Políticas Sobre o Impedimento de Dilma Rousseff no Twitter. In: Anais do XVIII Congresso de Ciências da Comunicação na Região Nordeste, Intercom – Sociedade Brasileira de Estudos Interdisciplinares da Comunicação, p S.I
Ruediger MA, Martins R, da Luz M, Grassi A (2014) Ação coletiva e polarização na sociedade em rede para uma teoria do conflito no brasil contemporâneo. Revista Brasileira de Sociologia 2(4):205–234
Schmitt J (2016) How to measure ideological polarization in party systems. In: ECPR Graduate Student Conference, University of Tartu, p SI
Stilo G, Velardi P (2016) Efficient temporal mining of micro-blog texts and its application to event discovery. Data Min Knowl Discov 30(2):372–402
Sunstein CR (2002) The law of group polarization. J Polit Philos 10(2):175–195
Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, pp 347–354
Yan X, Guo J, Lan Y, Cheng X (2013) A biterm topic model for short texts. In: Proceedings of the 22nd international conference on World Wide Web, WWW ’13. ACM Press, Rio de Janeiro, pp 1445–1456
Zhang L, Peng TQ, Zhang YP, Wang XH, Zhu JJ (2014) Content or context: Which matters more in information processing on microblogging sites. Comput Hum Behav 31:242–249
Zhang X, Chen X, Chen Y, Wang S, Li Z, Xia J (2015) Event detection and popularity prediction in microblogging. Neurocomputing 149:1469–1480
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Gisele L. Pappa and Pedro O.S. Vaz-de-Melo were funded by CNPq—the Brazilian Research Council—and FAPEMIG—the Research Council of the State of Minas Gerais.
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Moreira, R.C.N., Vaz-de-Melo, P.O.S. & Pappa, G.L. Elite versus mass polarization on the Brazilian impeachment proceedings of 2016. Soc. Netw. Anal. Min. 10, 92 (2020). https://doi.org/10.1007/s13278-020-00706-y
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DOI: https://doi.org/10.1007/s13278-020-00706-y