Semantic Embedding and Synthetic Augmentation for Longitudinal Survey Prediction (Student Abstract)

Longitudinal behavioral research relies on consistent measurement across time, yet real-world constraints force survey instruments to evolve, creating analytical discontinuities that compromise validity. This challenge intensifies during crises when researchers must rapidly incorporate new behavioral domains while preserving historical comparability. We address this problem through a dual-path architecture that maintains analytical continuity despite instrument changes. Using 15 waves of vaccination surveys as a testbed, we demonstrate how modern AI techniques can bridge both temporal gaps (from missing data) and semantic gaps (from question evolution). Our approach leverages LLM-generated semantic embeddings of survey questions, enabling the Deep & Cross Network to model responses as a joint function of item meaning, individual characteristics, and temporal context. The framework demonstrates exceptional resilience to missing data with semantic embeddings proving critical for bridging questionnaire evolution. To address data sparsity constraints, we develop cluster-informed synthetic data generation via hierarchical prompting that produces synthetic responses with strong distributional fidelity and delivers substantial performance gains through mixed real-synthetic training while reproducing empirical cluster dynamics.