Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections

Abstract

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.