Abstract
The prediction of presidential election outcome is key point of interest for politicians, electors and sponsoring companies. The 2018 Brazilian election presented a scenario with many uncertainties increasing prediction challenge. The utilization of social media as the promotion tools is another new scenario for both election and also prediction. In this paper, we present a Bayesian forecasting model based on the data from public opinion polls to predict the votes of undecided voters, about a third of the population. The migration of votes among candidates during the electoral period was also analyzed. By using the data from social media in the decision-making process, the proposed model and application show the capability to estimate the voting numbers of the main candidates with better accuracy than public opinion polls.
Keywords
- Forecasting
- Brazilian election
- Naive Bayes
- Social media
This is a preview of subscription content, access via your institution.
Buying options
References
Netto, V.: Lava Jato - o juiz Sérgio Moro e os bastidores da operação que abalou o Brasil, 1st edn. Rio de Janeiro, Primeira Pessoa - Selo da Editora Sextante (2016)
Igbokwe-Ibeto, C.J., et al.: Election and democratic consolidation in Nigeria: an analysis of the 2015 general elections. Arab. J. Bus. Manag. Rev. 5(10), 1–16 (2016)
Suryani, D.: Defending democracy: citizen participation in election monitoring in post-authoritarian Indonesia. J. Stud. Pemerintah. 6(1), 1–17 (2015). ISSN 2337-8220
Mendoza, M., Nieto-Barajas, L.E.: Quick counts in the Mexican presidential elections: a Bayesian approach. Elect. Stud. 43, 124–132 (2016)
Hummel, P., Rothschild, D.: Fundamental models for forecasting elections at the state level. Elect. Stud. 35, 123–139 (2014). https://doi.org/10.1016/j.electstud.2014.05.002
Linzer, D.A.: Dynamic Bayesian forecasting of presidential elections in the states. J. Am. Stat. Assoc. 108(501), 124–134 (2013)
Lock, K., Gelman, A.: Bayesian combination of state polls and election forecasts. Polit. Anal. 18(3), 337–348 (2010). https://doi.org/10.1093/pan/mpq002
Yang, P., Chen, Y.: A survey on sentiment analysis by using machine learning methods. In: Technology Networking Electronic and Automation Control Conference (ITNEC), pp. 117–121 (2017)
Hussein, D.M.E.D.M.: A survey on sentiment analysis challenges. J. King Saud Univ. Eng. Sci. 30(4), 330–338 (2018)
Joyce, B., Deng, J.: Sentiment analysis of tweets for the 2016 US presidential election. In: IEEE MIT Undergraduate Research Technology Conference, pp. 1–4 (2017)
Yaqub, U., et al.: Analysis of political discourse on twitter in the context of the 2016 US presidential elections. Gov. Inf. Q. 34(4), 613–626 (2017)
Machado, C., et al.: News and political information consumption in Brazil: mapping the first round of the 2018 Brazilian presidential election on Twitter. COMPROP Data Memo (2018)
IBOPE (2018). http://www.ibopeinteligencia.com
Datafolha (2018). http://datafolha.folha.com.br
Lewis, D.D.: Naive (Bayes) at forty: the independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) Machine Learning: ECML-1998. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, G., Costa, M., Drummond, A., Weigang, L. (2020). Predictive Model for Brazilian Presidential Election Based on Analysis of Social Media. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-32591-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32590-9
Online ISBN: 978-3-030-32591-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)