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Even though the Internet and social media have increased the amount of newsand information people can consume, most users are only exposed to content thatreinforces their positions and isolates them from other ideologicalcommunities. This environment has real consequences with great impact on ourlives like severe political polarization, easy spread of fake news, politicalextremism, hate groups and the lack of enriching debates, among others.Therefore, encouraging conversations between different groups of users andbreaking the closed community is of importance for healthy societies. In thispaper, we characterize and study users who break their community on Twitterusing natural language processing techniques and graph machine learningalgorithms. In particular, we collected 9 million Twitter messages from 1.5million users and constructed the retweet networks. We identified theircommunities and topics of discussion associated to them. With this data, wepresent a machine learning framework for social media users classificationwhich detects "community breakers", i.e. users that swing from their closedcommunity to another one. A feature importance analysis in three Twitterpolarized political datasets showed that these users have low values ofPageRank, suggesting that changes are driven because their messages have noresponse in their communities. This methodology also allowed us to identifytheir specific topics of interest, providing a fully characterization of thiskind of users.

Breaking the Communities: Characterizing community changing users using text mining and graph machine learning on Twitter