Handle with CARE: Can LLMs Reproduce How Online Communities React?

Title:Handle with CARE: Can LLMs Reproduce How Online Communities React?

Abstract:Large language models (LLMs) are increasingly used as proxies for computational social analysis, yet faithfully representing 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. Spanning 207 Reddit communities and covering 9,947 authentic reactions towards 2,166 news articles, CARE evaluates leading LLMs using a hierarchical taxonomy covering coarse attitudes and fine-grained communicative tones. Our empirical findings expose two critical failure modes in prevailing community-conditioning paradigms. First, while community context and targeted reasoning significantly enhance macro-level attitudinal and tonal profiling, these gains largely collapse at the instance level when predicting reactions to specific events. Second, the benefits of community conditioning are remarkably uneven: prompting strategies yield non-uniform shifts, where fidelity gains in some communities are offset by performance drops in others. This micro-macro divergence and community-level instability demonstrate that standard conditioning enables models to approximate static baseline profiles without capturing dynamic or equitable event reactions, establishing CARE as an essential diagnostic tool for community-aware social simulation. Our code and data are available at this https URL.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)

arXiv:2605.27388 [cs.CL]

(or arXiv:2605.27388v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.27388 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.