Modeling Community Attitude through Reaction Tone: A Human-AI Collaborative Framework for Evaluating LLM Alignment with Linguistic Behaviors in Online Communities
arXiv:2605.27388v1 Announce Type: cross 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 resp
The proliferation of LLMs into social analysis necessitates more nuanced validation methods to ensure their accuracy and reliability as proxies for human communities.
Accurate LLM alignment with authentic human linguistic behaviors is critical for their effective deployment in social analysis, preventing misinterpretations in critical domains.
The introduction of frameworks like CARE provides a more robust and 'reaction-centered' evaluation standard, moving beyond static labels to contextual socio-linguistic analysis.
- · AI researchers
- · Social scientists
- · Platforms utilizing LLMs for community management
- · Ethical AI developers
- · Companies with poorly aligned LLM social analysis tools
- · Researchers relying on simplistic LLM evaluation metrics
Improved accuracy and reliability of LLMs when analyzing complex human social dynamics.
Increased trust and adoption of sophisticated LLM applications in fields requiring deep social understanding.
The development of LLMs that can genuinely participate in and understand nuanced cultural and social contexts, blurring lines between human and artificial social intelligence.
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Read at arXiv cs.AI