Um Framework para Análise Bidimensional de Disseminação de Informações em Plataformas de Mídias Sociais

Abstract

Social media platforms have revolutionized how people connect and share information, but they also bring challenges when it comes to information dissemination. Many studies in the literature examine this phenomenon using network models. However, they often focus on a unidimensional analysis, considering only the volume of interactions on the edges, which does not fully capture the different aspects of this phenomenon, especially concerning the speed of dissemination. In this work, we propose a framework that allows for the bidimensional analysis of information dissemination on social media platforms, taking into account both the volume and the speed of interactions. Our framework is based on backbone extraction techniques to identify the most salient edges in both dimensions and classifies the edges into different dissemination profiles, allowing a detailed analysis of the topology and community presence for each profile. We applied it to two case studies covering critical information dissemination scenarios, notably on Twitter/X and Telegram. Our results show that the proposed framework is able to uncover different patterns of information dissemination. This emphasizes the importance of considering multiple dimensions simultaneously for a deeper understanding of the phenomenon.