Emotional Dynamics in Russian-Language Telegram Channels: Between Cohesion and Affective Polarization

This study proposes a novel methodological framework for analyzing key socio-psychological processes, namely in-group cohesion and affective polarization, within digital media during crises. By examining emotional dynamics in Russian-language Telegram channels (2.5k channels, 1.2M messages) across month preceding and following the onset of the Special Military Operation (SMO), we demonstrate an asymmetric transformation: intensified positive consolidation within ideologically aligned communities alongside heightened intergroup polarization, particularly in external engagements. Employing machine learning, text analytics, and network analysis, our work not only captures the specific reaction to the triggering event but also advances social identity theory by highlighting the fundamental role of emotional boundaries in shaping digital communities. These insights retain critical relevance for understanding social media dynamics in contemporary conflicts and societal divisions.