  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 break 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 the retweet networks. We identified their
communities and topics of discussion associated to them. With this data, we
present a machine learning framework for social media users classification
which detects "community breakers", i.e. 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 are driven because their messages have no
response in their communities. This methodology also allowed us to identify
their specific topics of interest, providing a fully characterization of this
kind of users.
