Characterizing Community Changing Users using Text Mining and Graph. Machine Learning on Twitter

FEDERICO ALBANESE; ESTEBAN FEUERSTEIN; LEANDRO LOMBARDI; BALENZUELA, PABLO

Santiago de Chile

Workshop; Alberto Mendelzon International Workshop on Foundations of Data Management (AMW 2023),; 2023

Even though the Internet and social media have increased the amount of news and information people can consume, most users are only exposed to content that reinforces their positions and isolates them from other ideological communities. This environment has real consequences with great impact on our lives like severe political polarization, easy spread of fake news, political extremism, hate groups and the lack of enriching debates, among others. Therefore, encouraging conversations between different groups of users and breaking the closed community is of importance for healthy societies. In this paper, we characterize and study users who change their community on Twitter using natural language processing techniques and graph machine learning algorithms. In particular, we collected 9 million Twitter messages from 1.5 million users and constructed retweet networks. We identified their communities and topics of discussion associated with them. With this data, we present a machine learning framework for social media users classification which detects users that swing from their closed community to another one. A feature importance analysis in three Twitter polarized political datasets showed that these users have low values of PageRank, suggesting that changes in community are driven because their messages have no resonance in their original communities.