Comunicação política no Facebook e previsão eleitoral - Análise de big data da eleição presidencial brasileira de 2018 no Brasil
Big data analysis of the 2018 Brazilian presidential election Brazil
DOI:
https://doi.org/10.34019/1981-4070.2019.v13.28589Keywords:
comunicação política, previsão eleitoral, opinião pública, mídias sociais, big dataAbstract
This article aims to understand how the quantitative analysis of political communication in social media, through indicators of engagement to the political speech, is able to predict the electoral outcome. This study is a big data analysis of the first round of the 2018 presidential election in Brazil. Specifically, around 10,000 posts were collected from official Facebook campaign pages between June 1 and October 7, 2018. Regarding voting intent, a series of daily representative of the Brazilian population opinion polls were conducted for the same period. In order to understand whether there is a causal relationship between candidate engagement on Facebook and voting intent, predictive analysis was performed by testing two empirical approaches: one with aggregated data and the other with 90 multiple linear regression prediction models. We conclude the analysis by comparing the actual election results with all predictive models. The results showed that both the aggregate and regressive approaches demonstrate that the candidate engagement rate on Facebook is a good electoral predictor. The results reinforce the theories that defend the relevance of data coming from political behaviour in social media as good electoral predictor.
Downloads
Download data is not yet available.
References
BIFET A., FAN W.: Mining Big Data: Current Status, and Forecast to the Future. SIGKDD Explorations 14(2), 1-5, 2013.
BORA, N.N. (2014) Summarizing public opinions in tweet. In Journal Proceedings of CICLing, 2014.
DIGRAZIA J, MCKELVEY K, BOLLEN J, ROJAS F. More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior. PLoS ONE 8(11): e79449. https://doi.org/10.1371/journal.pone.0079449, 2013.
DAYAN, D.; KATZ, E. Media events: the live broadcasting of history. Cambridge: Harvard University Press, 1992.
GAYO-AVELLO, D.(2014). “I wanted to predict elections with twitter and all I got was this lousy paper” – A balanced survey on elections prediction using twitter data. CoRR – http://arxiv.org/abs/1204.6441.
GAYO-AVELLO, D., METAXAS, P. AND MUSTAFARAJ, E. (2011). Limitis of election predictions using twitter. In Int. Conf. on Weblogs and Social Media (ICWSM), pages 490-493.
GHOSH, S., VISWANATH, B., KOOTI, F., SHARMA, N.K., KORLAM, G., BENEVENUTO, F., GANGULY, N., AND GUMMADI, K.P. (2012). Understanding and combating link farming in the twitter social network. In Int. Conf. on World Wide Web, WWW’ 12, pages 61-70.
GOMES, N. Formas persuasivas de comunicação política. Propaganda Política e Publicidade eleitoral. Porto Alegre: EDUPUCRS, 2004.
GUO, L., MCCOMBS, M. (2015). The Power of Information Networks: New Directions for Agenda Setting. (L. Guo & M. McCombs, Eds.). Routledge.
JUNGHERR, A., JÜRGENS, P. AND SCHOEN, H. (2012). Why the pirate party won the german election of 2009 or the trouble with predictions: A response to Tumasjan, A., Sprenger, T.O., Sander, P.G., & Welpe, I.M.”Predicting elections with twitter: What 140 characters reveal about political sentiment”. Soc. Sci. Comput. Rev.,30(2):229-234.
KRISTENSEN JB, ALBRECHTSEN T, DAHL-NIELSEN E, JENSEN M, SKOVRIND M, BORNAKKE T. Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems. PLoS ONE 12(9): e0184562, 2017.
LUMEZANU, C., FEAMSTER, N., AND KLEIN, H. (2012). #bias: Measuring the tweeting behavior of propagandist. In Int. conf. on Weblogs and Social Media (ICWSM), pages 210-217.
MUKHERJEE, S., MALU, A., A.R., B., AND BHATTACHARYYA, P. (2013), Twisent: a multistage system for analizing sentiment in twitter. In Int. conf. on Information and knowledge management, CIKM’ 12, pagens 2531-2534.
NORRIS, P. Democratic divide? The impact of the Internet on parliaments worldwide. Harvard, Harvard University, John Kennedy School of Government, 2000.
SALGADO, S. Campanhas eleitorais e cobertura mediática: abordagens teóricas e contributos para a compreensão das interações entre política e media. Revista Brasileira de Ciência Política, n.9, pp. 229- 253.
TRUMPER, D. S., MEIRA, W., & ALMEIDA, V. From Total Hits to Unique Visitors Model for Election’s Forecasting. Proceedings of the ACM WebSci’11. Koblenz, 2011.
TRUMPER, D. S., MEIRA, W., & ALMEIDA, V. From Total Hits to Unique Visitors Model for Election’s Forecasting. Proceedings of the ACM WebSci’11. Koblenz, 2011.
TUMASJAN A, SPRENGER T. O., SANDNER P. G., AND WELPE I. M.. Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape. Social Science Computer Review, 2010.
TUMASJAN, A., SPRENGER, T.O., SANDER, P.G., & WELPE, I.M.(2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. Int. conf. on Weblogs and Social Media (ICWSM), pages 178-185.
WEIMANN, G., BROSIUS, H.-B. (2016). A New Agenda for Agenda-Setting Research in the Digital Era. In HENN, P., VOWE, G. (Eds.), Political Communication in the Online World: Theoretical Approaches and Research Designs (pp. 26–44). New York: Routledge.
YASSERI T, BRIGHT J. Wikipedia traffic data and electoral prediction: towards theoretically informed models. EPJ Data Sci, 2016.
BORA, N.N. (2014) Summarizing public opinions in tweet. In Journal Proceedings of CICLing, 2014.
DIGRAZIA J, MCKELVEY K, BOLLEN J, ROJAS F. More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior. PLoS ONE 8(11): e79449. https://doi.org/10.1371/journal.pone.0079449, 2013.
DAYAN, D.; KATZ, E. Media events: the live broadcasting of history. Cambridge: Harvard University Press, 1992.
GAYO-AVELLO, D.(2014). “I wanted to predict elections with twitter and all I got was this lousy paper” – A balanced survey on elections prediction using twitter data. CoRR – http://arxiv.org/abs/1204.6441.
GAYO-AVELLO, D., METAXAS, P. AND MUSTAFARAJ, E. (2011). Limitis of election predictions using twitter. In Int. Conf. on Weblogs and Social Media (ICWSM), pages 490-493.
GHOSH, S., VISWANATH, B., KOOTI, F., SHARMA, N.K., KORLAM, G., BENEVENUTO, F., GANGULY, N., AND GUMMADI, K.P. (2012). Understanding and combating link farming in the twitter social network. In Int. Conf. on World Wide Web, WWW’ 12, pages 61-70.
GOMES, N. Formas persuasivas de comunicação política. Propaganda Política e Publicidade eleitoral. Porto Alegre: EDUPUCRS, 2004.
GUO, L., MCCOMBS, M. (2015). The Power of Information Networks: New Directions for Agenda Setting. (L. Guo & M. McCombs, Eds.). Routledge.
JUNGHERR, A., JÜRGENS, P. AND SCHOEN, H. (2012). Why the pirate party won the german election of 2009 or the trouble with predictions: A response to Tumasjan, A., Sprenger, T.O., Sander, P.G., & Welpe, I.M.”Predicting elections with twitter: What 140 characters reveal about political sentiment”. Soc. Sci. Comput. Rev.,30(2):229-234.
KRISTENSEN JB, ALBRECHTSEN T, DAHL-NIELSEN E, JENSEN M, SKOVRIND M, BORNAKKE T. Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems. PLoS ONE 12(9): e0184562, 2017.
LUMEZANU, C., FEAMSTER, N., AND KLEIN, H. (2012). #bias: Measuring the tweeting behavior of propagandist. In Int. conf. on Weblogs and Social Media (ICWSM), pages 210-217.
MUKHERJEE, S., MALU, A., A.R., B., AND BHATTACHARYYA, P. (2013), Twisent: a multistage system for analizing sentiment in twitter. In Int. conf. on Information and knowledge management, CIKM’ 12, pagens 2531-2534.
NORRIS, P. Democratic divide? The impact of the Internet on parliaments worldwide. Harvard, Harvard University, John Kennedy School of Government, 2000.
SALGADO, S. Campanhas eleitorais e cobertura mediática: abordagens teóricas e contributos para a compreensão das interações entre política e media. Revista Brasileira de Ciência Política, n.9, pp. 229- 253.
TRUMPER, D. S., MEIRA, W., & ALMEIDA, V. From Total Hits to Unique Visitors Model for Election’s Forecasting. Proceedings of the ACM WebSci’11. Koblenz, 2011.
TRUMPER, D. S., MEIRA, W., & ALMEIDA, V. From Total Hits to Unique Visitors Model for Election’s Forecasting. Proceedings of the ACM WebSci’11. Koblenz, 2011.
TUMASJAN A, SPRENGER T. O., SANDNER P. G., AND WELPE I. M.. Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape. Social Science Computer Review, 2010.
TUMASJAN, A., SPRENGER, T.O., SANDER, P.G., & WELPE, I.M.(2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. Int. conf. on Weblogs and Social Media (ICWSM), pages 178-185.
WEIMANN, G., BROSIUS, H.-B. (2016). A New Agenda for Agenda-Setting Research in the Digital Era. In HENN, P., VOWE, G. (Eds.), Political Communication in the Online World: Theoretical Approaches and Research Designs (pp. 26–44). New York: Routledge.
YASSERI T, BRIGHT J. Wikipedia traffic data and electoral prediction: towards theoretically informed models. EPJ Data Sci, 2016.
Downloads
Published
2019-12-30
How to Cite
MAGALHÃES FIRMINO, L.; MURTA, F. Comunicação política no Facebook e previsão eleitoral - Análise de big data da eleição presidencial brasileira de 2018 no Brasil: Big data analysis of the 2018 Brazilian presidential election Brazil. Lumina, [S. l.], v. 13, n. 3, p. 47–63, 2019. DOI: 10.34019/1981-4070.2019.v13.28589. Disponível em: https://periodicos.ufjf.br/index.php/lumina/article/view/28589. Acesso em: 17 aug. 2024.
Issue
Section
Dossiê: Comunicação Política, Eleições 2018 e Campanha Permanente
License
Autores que publicam nesta revista concordam com os seguintes termos:
- Autores mantém os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.
- Autores têm autorização para assumir contratos adicionais separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista.
- Autores têm permissão e são estimulados a publicar e distribuir seu trabalho online (ex.: em repositórios institucionais ou na sua página pessoal) a qualquer ponto antes ou durante o processo editorial, já que isso pode gerar alterações produtivas, bem como aumentar o impacto e a citação do trabalho publicado (Veja O Efeito do Acesso Livre).
