Unveiling Community Dynamics on Instagram Political Network

Online Social Networks (OSNs) allow users to generate and consume content in an easy and personalized way. Among OSNs, Instagram has seen a surge in popularity, and political actors exploit it to reach people at scale, bypassing traditional media and often triggering harsh debates with and among followers. Uncovering the structural properties and dynamics of such interactions is paramount for understanding the online political debate. This is a challenging task due to both the size of the network and the nature of interactions. In this paper, we define a probabilistic model to extract the backbone of the interaction network among Instagram commenters and, after that, we uncover communities. We apply our model to 10 weeks of comments centered around election times in Brazil and Italy. We monitor both politicians and other categories of influencers, finding persistent commenters, i.e., those who often comment together on Instagram posts. Our methodology allows us to unveil interesting facts: i) commenters’ networks are split into few communities; ii) community structure in politics is weaker than in general profiles, indicating that the political debate is a blur, with some commenters bridging strongly opposed political actors; and iii) communities engaging on political profiles are bigger, more active and more stable during electoral period.