Un Enfoque Innovador para Inferir Redes a partir de Hilos de Tweets en X

Resumen

This article focuses on Analysis of Online Social Networks (AOSNs) and proposes an innovative method to infer networks from tweet threads. These threads, which consist of se-quential posts, are used to build networks where the first tweet acts as the starting point and sub-sequent tweets as replies or extensions. A data set obtained from Twitter(X) was used, focused on the topic "chatGPT". The methodology involves identifying the "seed users" who start the threads and those who participate in the conversations. Through PhantomBuster, user profiles were ex-tracted, leading to the creation of .xlsx files that represent nodes and connections on the network. For visualization and analysis, Gephi software was used. The study seeks to understand how in-formation spreads on digital social networks, identify prominent users, and assess diffusion. Using social media metrics such as centrality, closeness, clustering coefficient, and influence, a deeper understanding of Twitter(X) propagation patterns is gained. The results indicate the feasi-bility of creating networks from threads of tweets, enriching the understanding of how informa-tion flows and affects interactions in digital social networks.