Identifying and Characterizing new Expressions of Community Framing during Polarization

Abstract

Chile experienced a series of important protests between October and December 2019, fueled by the country's significant social inequity.This social outburst, as it was called, radically affected the nation's status quo. A large portion of the population demanded a new constitution and changes to the current government, whereas another part of the population rejected these social demands and institutional reforms. This created a highly polarized scenario that was evidenced in online social media interactions. Here we analyze group polarization in social networks by studying the 2019 Chilean social unrest movement. Specifically, we propose an unsupervised approach for identifying and characterizing community framing (i.e., discovering and understanding polarized concepts) in online social networks. Our approach is based on the sequential application of community detection, topic modeling, and word embedding methods. The novelty of having an unsupervised approach is that it facilitates the performance of scalable and objective framing analyses with minimal human intervention, as it does not require prior domain or network knowledge. Using this methodology, we observe that an apparently similar conversation topic across communities can actually have completely different meanings for each group. We noted, for instance, that while an online community linked the term gente (people) with communism and terrorism, the other associated it with police and military aggression to citizenship. Analyzing controversial issues that emerge naturally from conversations in online communities offers a deeper and great-scale understanding of today's political and societal discussions. In this direction, our findings have implications for contextualizing real-world social issues on online platforms, describing how users discuss similar concepts with opposing views. In addition, despite that communities with opposing views discussed similar concepts, our results also provide clues that conversations could converge to common themes, bridging the gap in polarized discussions.