| Resumen |
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). |
