Characterization of users and speeches in online social networks
social networks, graphs, complex networks, machine learning, Twitter
Online social networks, such as WhatsApp, Twitter and Facebook are used daily by thousands of people as a means of entertainment, communication, access to information and claims. In this environment, anyone has the power to generate and propagate news, resulting in new opportunities and dilemmas, such as the misinformation epidemic. Within this ecosystem, bots and strategies like Reflexive Control (RC) can be used by different agents in order to confuse, manipulate and distort public opinion on matters of interest. Therefore, approaches that help to characterize this environment with a focus on the user become essential. To this end, we present SARA (Automated System with Complex Networks and Analytics), an approach that allows the characterization of large-scale events centered on users in online social networks, especially on Twitter. Combining complex networks, text mining, machine learning with an communities approach to SARA, allows the identification of automated accounts (bots), mapping of antagonistic views on a given subject, extraction of topics of interest, identification of propagation patterns information and user interaction in a semi-automated way. The present work also presents its application in the analysis of three real issues in Brazil. About 3 million tweets about STF, anxiety and vaccination were analyzed.