A Weakly-Labeled Stance Dataset during the 2019 South American Protests

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 government of each country (for or against), we construct a weakly labeled stance dataset with hundreds of thousands of users. Moreover, through the synergistic usage of network-focused methods applied on news sharing patterns and language-focused methods, we validate our labeling methodology by showing that these stances partition the users into meaningful communities. That is, we show that polarization in users' news sharing patterns was consistent with their stances towards the government and that polarization in their language mainly manifested along ideological, political, or protest-related lines.