Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections
| Autor: | Hugo Arboleda, Javier Diaz Cely, Didier Grimaldi |
|---|---|
| Přispěvatelé: | Universitat Ramon Llull. La Salle, Universidad Icesi |
| Rok vydání: | 2020 |
| Předmět: |
Value (ethics)
Information Systems and Management Scrutiny lcsh:Computer engineering. Computer hardware Presidential election Computer Networks and Communications Computer science Election media_common.quotation_subject lcsh:TK7885-7895 02 engineering and technology Lexicon lcsh:QA75.5-76.95 Politics Big social data 00 - Ciència i coneixement. Investigació. Cultura. Humanitats 65 - Gestió i organització. Administració i direcció d'empreses. Publicitat. Relacions públiques. Mitjans de comunicació de masses 020204 information systems Voting Machine learning Aprenentatge automàtic 0202 electrical engineering electronic engineering information engineering media_common Presidential system lcsh:T58.5-58.64 lcsh:Information technology 004 - Informàtica Sentiment analysis Dades massives 32 - Política Espanya. Parlament -- Eleccions 2019 Data science Eleccions -- Xarxes socials Hardware and Architecture Spain 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Prediction 62 - Enginyeria. Tecnologia Information Systems |
| Zdroj: | RECERCAT (Dipòsit de la Recerca de Catalunya) Recercat: Dipósit de la Recerca de Catalunya Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) Journal of Big Data, Vol 7, Iss 1, Pp 1-19 (2020) Recercat. Dipósit de la Recerca de Catalunya instname |
| Popis: | The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes. |
| Databáze: | OpenAIRE |
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