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Predictive model for Brazilian presidential election based on analysis of social media

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 ne...

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Main Authors: Silva, Guilherme, Costa, Mirele, Drummond, André, Li Weigang
Format: Trabalho
Language: Inglês
Published: Springer 2020
Subjects:
Previsão
Eleições - Brasil
Teorema de Bayes
Mídia social
Online Access: https://repositorio.unb.br/handle/10482/37047
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spelling ir-10482-370472020-03-05T11:54:26Z Predictive model for Brazilian presidential election based on analysis of social media Silva, Guilherme Costa, Mirele Drummond, André Li Weigang Previsão Eleições - Brasil Teorema de Bayes Mídia social 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. 2020-03-05T11:51:27Z 2020-03-05T11:51:27Z 2019 Trabalho SILVA, Guilherme et al. Predictive model for Brazilian presidential election based on analysis of social media. In: INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, 15., 2019, Kunming. https://repositorio.unb.br/handle/10482/37047 Inglês https://link.springer.com/chapter/10.1007/978-3-030-32591-6_5 Acesso Restrito Springer
institution REPOSITORIO UNB
collection REPOSITORIO UNB
language Inglês
topic Previsão
Eleições - Brasil
Teorema de Bayes
Mídia social
spellingShingle Previsão
Eleições - Brasil
Teorema de Bayes
Mídia social
Silva, Guilherme
Costa, Mirele
Drummond, André
Li Weigang
Predictive model for Brazilian presidential election based on analysis of social media
description 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.
format Trabalho
author Silva, Guilherme
Costa, Mirele
Drummond, André
Li Weigang
author_sort Silva, Guilherme
title Predictive model for Brazilian presidential election based on analysis of social media
title_short Predictive model for Brazilian presidential election based on analysis of social media
title_full Predictive model for Brazilian presidential election based on analysis of social media
title_fullStr Predictive model for Brazilian presidential election based on analysis of social media
title_full_unstemmed Predictive model for Brazilian presidential election based on analysis of social media
title_sort predictive model for brazilian presidential election based on analysis of social media
publisher Springer
publishDate 2020
url https://repositorio.unb.br/handle/10482/37047
_version_ 1672206156813041664
score 13.657419

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