Adverse Online Social Interactions: A Multi-Level Evolutionary Analysis of Local Patterns, Diffusion, and Community Disruption
For researchers studying online community dynamics, this work provides a multi-scale understanding of how adverse interactions shape community evolution, though it is incremental as it combines existing concepts.
This study proposes a multi-level framework to analyze adverse social interactions (ASIs) in online communities, examining local patterns, diffusion, and subgroup disruption using data from X and Bluesky. Results show structural disconnection and toxic communication provide complementary signals, with structural negativity more persistently marking subgroup disruption.
Adverse social interactions (ASIs) can shape how online communities evolve over the time. However, structural-based ASIs and content-based ASIs are often studied separately and at a single analytical scale. In this study, we propose a multi-level framework to examine how adverse social interactions appear locally, spread through neighborhoods, and disrupt cohesive subgroups. Using large-scale datasets from X and Bluesky, we analyze friend and foe patterns at the micro level, peer influence through matched triadic designs at the meso level, and subgroup disruption against random and recommendation-based references at the macro level. Our results show that structural disconnection and toxic communication provide complementary signals: structural negativity more persistently marks subgroup disruption, while toxic communication captures broader conflict both within and across communities. These findings suggest that adverse social interactions are multi-scale processes that influence how online communities form, fracture, and evolve. Our source code is publicly available at https://github.com/XueqiC/Adverse-Social-Interactions.