Social Network Analysis for Automatic Ranking of Political Stakeholders: a Case Study

Abstract

This article exposes the way in which the creation of a new method for calculating the popularity of stake holders in social networks can support political data analysis experts. The definition of a new formula for estimating popularity allowed us to have a new method that, together with other previously existing ones, allows us to build a multidimensional interpretation of reality. The construction of a method that would seem like a computational scientific curiosity has significant impacts for experts who carry out political analysis. The new ranking algorithm called BOPRank made it possible to identify political actors in a different way than known algorithms. While a wellknown algorithm showed popularity as a result of the work of campaign teams on social networks, the new algorithm reflected popularity obtained as a result of the reaction of the public on social networks.