Social Identity Reconstruction through Large Language Models: A Computational Approach to Self-Perception Analysis in Online Communities
Keywords:
large language models, social identity, self-perception, online communities, system architecture, fairness, governance, computational social scienceAbstract
The proliferation of online communities has transformed how individuals construct, express, and revise social identity. Large language models (LLMs) now offer unprecedented capabilities for analyzing and modeling self-perception narratives at scale, yet their integration into identity-sensitive computational systems raises profound architectural, governance, and ethical challenges. This paper presents a system-level examination of a computational pipeline that ingests user-generated discourse from digital platforms and reconstructs latent identity signals through LLM-based semantic analysis. The discussion emphasizes structural trade-offs in data collection and model deployment, highlighting tensions between accuracy, fairness, interpretability, and privacy. A multi-tier architecture is proposed, coupling federated data governance with modular prompt engineering and human-in-the-loop validation. Robustness is addressed through adversarial stress testing and fairness audits that account for sociolinguistic diversity across communities. Policy implications are examined, including the reconfiguration of identity agency, potential for algorithmic reification of stereotypes, and the need for participatory infrastructure design. By framing identity reconstruction as a socio-technical system, the paper provides a forward-looking perspective on how LLM-driven self-perception analysis can be deployed sustainably and equitably, without undermining the fluid, co-constructed nature of online identity. This work contributes a comprehensive architectural and policy-aware blueprint for researchers and platform designers seeking to harness LLMs for identity research while upholding community values and individual autonomy.
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