Adverse Online Social Interactions: A Multi-Level Evolutionary Analysis of Local Patterns, Diffusion, and Community Disruption

2026-06-18

AI Summary

This study addresses the limitations of existing research, which typically examines structural or content-based negative interactions in isolation and at a single scale, thereby failing to fully capture their impact on online community evolution. The authors propose the first multi-level analytical framework that integrates both structural and content-based negative interactions, modeling them cohesively across micro (local interaction patterns), meso (diffusion mechanisms), and macro (community dissolution) scales. Leveraging large-scale data from X and Bluesky, the study employs empirical analyses based on triangle matching, structural disconnection detection, and benchmark comparisons against recommendation systems. Findings reveal that structural negative interactions more persistently signal subgroup dissolution, whereas toxic content broadly reflects both intra- and inter-group conflicts, demonstrating that negative interactions constitute a key multi-scale driver of community formation and fragmentation.

Abstract

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.

Problem

• Adverse Social Interactions
• Multi-Level Analysis
• Online Communities
• Structural Negativity
• Toxic Communication

Innovation

• multi-level analysis
• adverse social interactions
• structural disconnection
• toxic communication
• online community disruption