Back to #6D: Predicting Venezuelan states political election results through Twitter

The large adoption of Twitter during electioneering has created an unprecedented opportunity to capture the citizen's behaviour nationwide. The real-time access to information published by citizens has motivated researchers to design methods in order to enrich traditional political polling with insights from this rich source of data. However, less work has been done to capture the political scenario in Latin American countries, given that some methods rely on the use of English words, the reproducibility of such studies in Spanish speaking countries is a challenging task. Therefore, we propose a framework in which we apply social network analysis techniques and unsupervised machine learning to infer the political alignment at state level during Venezuelan Parliamentary election, which were performed on December 6, 2015. This electoral process took place in the middle of an acute political polarization in the country, the masses were organized around two political coalitions with opposite ideology: Government and opposition. In order to discover automatically the corresponding state political preferences, we analyze 60K tweets posted within the Venezuelan geographic boundaries during one week before the election day. Applying our framework, we are able to infer a given state political alignment starting from the quantified differences in communication patterns and linguistic profiles of the state aggregated tweets. We demonstrate that the online political atmosphere reflects the offline tendency at state scale given that we are able to predict the election tendency in Venezuela states with an accuracy of 87.5% with respect to official election results publicly available.