JUCS - Journal of Universal Computer Science 23(3): 304-326, doi: 10.3217/jucs-023-03-0304
Electoral Preferences Prediction of the YouGov Social Network Users Based on Computational Intelligence Algorithms
expand article infoSonia Ortiz-Ángeles, Yenny Villuendas-Rey§, Itzamá López-Yáñez, Oscar Camacho-Nieto§, Cornelio Yáñez-Márquez|
‡ Instituto Politecnico Nacional, CIDETEC-IPN, Ciudad de Mexico, Mexico§ CIDETEC-IPN, Ciudad de Mexico, Mexico| CIC-IPN, Ciudad de Mexico, Mexico
Open Access
The contemporary world has witnessed technological advances, such as Online Social Networks (OSN), whose influence in almost every action of the human being is remarkable. Among the human activities most significantly impacted by OSNs are: entertainment, human relationships, education, and political activities, including those related to electoral campaigns and electoral preferences prediction. The research contribution of the current paper regards the usefulness of OSNs users generated data to predict the political context. More specifically, 25 Computational Intelligence (CI) algorithms are used to predict voting intentions on the United States primary presidential elections for 2016, taking as input the data sets generated by 1200 users of the YouGov OSN, as well as the answers they gave to an online study run by the American National Election Studies (ANES). The application of the 25 supervised classification algorithms is done over the Waikato Environment for Knowledge Analysis (WEKA), using a stratified 5-fold cross validation scheme. Also, the experimental results obtained were validated in order to identify significant differences in performance by mean of a non-parametric statistical test (the Friedman test), and a post-hoc test (the Holm test). The hypothesis testing analysis of the experimental results indicates that predicting voting intentions in favour of a democrat or republican candidate is simpler than predicting the particular candidate, given that the prediction performances for a democrat or republican candidate (best performances of 80% and 78%, respectively) are better than those given when predicting a specific candidate (70% for democrat candidates and 56% for republican candidates).
electoral preferences, prediction, online social networks, computational intelligence