AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909922215591936 |
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| author | Huq, Faria Claggett, Elijah L. Shirado, Hirokazu |
| author_facet | Huq, Faria Claggett, Elijah L. Shirado, Hirokazu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_21984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups Huq, Faria Claggett, Elijah L. Shirado, Hirokazu Social and Information Networks Computation and Language 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. |
| title | AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups |
| topic | Social and Information Networks Computation and Language |
| url | https://arxiv.org/abs/2510.21984 |