A framework for the analysis of information propagation in social networks combining complex networks and text mining techniques - Barbosa, Carlos Magno G.; Felix, Lucas Gabriel da S.; Xavier, Carolina R.; Vieira, Vinícius da F. (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2019: Proceedings of the 25th Brazilian Symposium on Multimedia and the Web, 2019)
Online social networks like Twitter, Facebook and WhatsApp are among the greatest innovations of the modern internet. Through these applications, users can consume and be major news broadcasters. These networks are sensitive to real-time events and generate a large amount of data at all times. The ability to extract information from this large amount of data is essential for the survival of companies and the modernization of public policies. With this purpose, this work presents the construction of a framework that combines complex networks and data mining to analyze the content and the propagation of information in social networks, especially in Twitter. As a practical case, the methodology is applied to the analysis of messages posted on twitter related to pension reform in Brazil. As a result, the framework was able to identify the main topics of Internet discussion and the positioning within certain communities regarding the subject. The main feeling surrounding the discussion turned out to be negative and pro-retirement users were more involved in supportive and anti-reform communities.