  Research across different disciplines has documented the expanding
polarization in social media. However, much of it focused on the US political
system or its culturally controversial topics. In this work, we explore
polarization on Twitter in a different context, namely the protest that
paralyzed several countries in the South American region in 2019. By leveraging
users' endorsement of politicians' tweets and hashtag campaigns with defined
stances towards the protest (for or against), we construct a weakly labeled
stance dataset with millions of users. We explore polarization in two related
dimensions: language and news consumption patterns. In terms of linguistic
polarization, we apply recent insights that leveraged machine translation
methods, showing that the two communities speak consistently "different"
languages, mainly along ideological lines (e.g., fascist translates to
communist). Our results indicate that this recently-proposed methodology is
also informative in different languages and contexts than originally applied.
In terms of news consumption patterns, we cluster news agencies based on
homogeneity of their user bases and quantify the observed polarization in its
consumption. We find empirical evidence of the "filter bubble" phenomenon
during the event, as we not only show that the user bases are homogeneous in
terms of stance, but the probability that a user transitions from media of
different clusters is low.
