Beyond Aggregate Feedback: A Spatially-Aware Computational Framework for Understanding Citizen-State Interaction on Social Media

In the era of digital governance, social media platforms have become critical arenas for citizen-state interaction and policy feedback. However, governments often struggle to translate massive, unstructured public discourse into actionable intelligence, frequently relying on aggregated metrics that obscure critical regional nuances. This study introduces a spatially-aware computational framework to bridge this gap. Integrating Large Language Model (LLM) based few-shot learning with SHAP (SHapley Additive exPlanations) and GIS analysis, we examine the public reception of China’s 2025 national childcare subsidy policy using a dataset of 352,448 comments. Our analysis reveals a stark divergence between information-seeking behaviors (high volume) and substantive legitimacy debates (high engagement). Crucially, we demonstrate that the socioeconomic drivers of this discourse—such as gender ratios and educational attainment—are highly spatially heterogeneous, necessitating distinct governance responses across different regions. This research contributes to the field of government information systems by validating a novel, replicable methodology for precision policy analytics, offering policymakers a tool to move beyond national averages and achieve more responsive, context-specific governance.