AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups
Faria Huq, Elijah L. Claggett, Hirokazu Shirado

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOpinion Dynamics and Social Influence · Language and cultural evolution · Social Power and Status Dynamics
