Modeling Community Attitude through Reaction Tone: A Human-AI Collaborative Framework for Evaluating LLM Alignment with Linguistic Behaviors in Online Communities
Large language models (LLMs) are increasingly used as proxies for computational social analysis, but their ability to faithfully represent human communities' 'thick descriptions' remains a critical challenge. This paper introduces CARE (Community-Aware Reaction Evaluation), a reaction-centered framework that benchmarks LLM-simulated discourse against authentic community responses to real-world news. By characterizing a fine-grained spectrum of illocutionary tones, the diagnosis reveals a persistent 'realism gap': steering LLMs with explicit community prompts fails to inherently improve simulation fidelity. Analysis further identifies divergent behavioral signatures among frontier models, suggesting current alignment strategies are insufficient for capturing the sociolinguistic dynamics of online groups.
Article intelligence
Key points
- CARE framework evaluates LLM simulation fidelity by analyzing authentic community reaction tones
- Current LLM alignment strategies fail to adequately capture online community sociolinguistic dynamics
- Human-AI collaboration validates a fine-grained illocutionary tone spectrum, revealing a 'realism gap'
- Frontier models exhibit divergent behavioral signatures when simulating community responses
Why it matters
This matters because CARE framework evaluates LLM simulation fidelity by analyzing authentic community reaction tones.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27388] Modeling Community Attitude through Reaction Tone: A Human-AI Collaborative Framework for Evaluating LLM Alignment with Linguistic Behaviors in Online Communities
[Submitted on 12 Apr 2026]
Title:Modeling Community Attitude through Reaction Tone: A Human-AI Collaborative Framework for Evaluating LLM Alignment with Linguistic Behaviors in Online Communities
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Abstract:Large language models (LLMs) are increasingly utilized as proxies for computational social analysis; yet, their ability to faithfully represent the "thick descriptions" (Geertz, 1973) of human communities remains a critical challenge. Current evaluations often reduce social identity to static labels, sidelining how real-world groups navigate social shifts. To bridge this gap, we introduce CARE (Community-Aware Reaction Evaluation), a reaction-centered framework that benchmarks LLM-simulated discourse against the authentic, event-contingent responses of distinct communities to real-world news. By characterizing a fine-grained spectrum of illocutionary tones and the underlying attitudes they manifest--validated through human-AI collaboration--our diagnosis reveals a persistent "realism gap": steering LLMs with explicit community prompts fails to inherently improve simulation fidelity. Analysis further identifies divergent behavioral signatures among frontier models, suggesting that current alignment strategies remain insufficient for capturing the sociolinguistic dynamics of online groups.
Comments: Preprint
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
Cite as: arXiv:2605.27388 [cs.CL]
(or arXiv:2605.27388v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.27388
arXiv-issued DOI via DataCite
Submission history
From: Nuan Wen [view email] [v1] Sun, 12 Apr 2026 07:46:12 UTC (303 KB)
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