Drawing digital lines: pattern analysis of divisive rhetoric in social network discussions

Social dialogue is a cornerstone for political decision-making and maintaining mutual understanding between diverse societal groups when addressing collective challenges. However, this dialogue is increasingly strained in digital environments where users regularly encounter opposing viewpoints. While research has examined how political actors strategically leverage divisive rhetoric, less attention has been paid to how ordinary users utilize these devices in everyday online interactions. This study investigates how users employ divisive rhetorical strategies across social networks, examining the relationships between topic controversiality, user stance, and interactive patterns. Through a large-scale analysis of 146K YouTube comments on immigration and climate change discussions—two highly polarizing topics in contemporary discourse. The research combines computational methods for rhetoric mining with network analysis to track patterns of user interaction and manifestation of divisive rhetoric. Our analysis reveals three key findings: (1) Controversial topics elicit significantly higher frequencies of divisive rhetorical strategies compared to non-controversial ones, with distinct patterns across topics; (2) Users demonstrating strong stance commitment (Pro and Contra) use significantly more divisive rhetoric with parallel patterns, regardless of ideological position, distinguishing them from neutral users; (3) Users strategically adapt their rhetorical behavior to their interlocutor’s stance, suggesting that stance intensity rather than specific ideological content drives rhetorical similarity. Framed through Social Identity Theory, we conceptualize these wedge rhetorical devices as an interactive toolkit that users deploy to navigate social positioning in deindividualized discussions, either reinforcing solidarity among users sharing similar positions or creating distinctions from those holding opposing views. This study shows how computational methods can effectively track and analyze the ways citizens strategically navigate social positioning on sensitive issues, contributing to our understanding of online political discourse dynamics.