Exploring Polarization of Users Behavior on Twitter During the 2019 South American Protests

TL;DR

This study investigates Twitter user polarization during the 2019 South American protests, analyzing linguistic differences and news consumption patterns to understand ideological divides and filter bubble effects.

Contribution

It introduces a novel weakly labeled stance dataset for South American protests and applies linguistic and clustering analyses to reveal polarization dynamics.

Findings

Communities speak different languages along ideological lines

User bases are homogeneous in stance and media consumption

Low transition probability between different media clusters

Abstract

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.