AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups

TL;DR

This study shows that AI-assisted messaging influences social group structures during political discussions, with personalized and relational AI guidance leading to different patterns of cohesion and division.

Contribution

It demonstrates how real-time AI message suggestions can alter group dynamics and social structures in online political discussions.

Findings

AI assistance increases message frequency and clustering based on stance.

Relational AI fosters more heterogeneous and receptive interactions.

AI-mediated communication impacts macro-level social organization.

Abstract

Group segregation or cohesion can emerge from micro-level communication, and AI-assisted messaging may shape this process. Here, we report a preregistered online experiment (N = 557 across 60 sessions) in which participants discussed controversial political topics over multiple rounds and could freely change groups. Some participants received real-time message suggestions from a large language model (LLM), either personalized to their stance (individual assistance) or incorporating their group members' perspectives (relational assistance). We find that small variations in AI-mediated communication cascade into macro-level differences in group composition. Participants with individual assistance send more messages and show greater stance-based clustering, whereas those with relational assistance use more receptive language and form more heterogeneous ties. Hybrid expressive processes-jointly produced by humans and AI-can reshape collective organization. The patterns of structural division and cohesion depend on how AI incorporates users' interaction context.