A survey on the use of data and opinion mining in social media to political electoral outcomes prediction

Election polls are the de facto mechanisms to predict political outcomes. Traditionally, they are conducted with personal interviews and questionnaires. This process is costly and time consuming, demanding the development of alternative approaches faster and less expensive. On the other hand, social media emerge as important tools for people to express their opinions about candidates in electoral scenarios. In this context, there is an increasing number of election prediction approaches using social media and opinion mining, modeling this problem in different ways. In this work, we present a survey on approaches to election predictions and discuss many possibilities of decisions in the general process of constructing solutions to this end, including the quantity of collected data, the specific social media used, the collection period, the algorithms and prediction approaches adopted, among others aspects. Our overview allowed us to identify the main factors that should be considered when predicting elections outcomes supported by social media content, as well as the main open issues and limitations of the approaches found in the literature for data science communities. In brief, the main challenges that we have found include but are not limited to: labeling data reliably during the short period of electoral campaigns, absence of a robust methodology to collect and analyze data, non-availability of domain (labeled) datasets, a lack of a pattern to evaluate the obtained results and exploration of new machine learning algorithms and methods for tackling the peculiarities of this scenario.