Artificial Intelligence for Virtual Reality-Enabled Social Listening in Smart Tourism

This systematic review investigates the application of recurrent neural network (RNN) architectures in virtual reality (VR)–enabled social listening systems within smart tourism contexts. The study synthesises empirical and technical evidence on how RNN-based models, including long short-term memory and gated recurrent units, are employed to analyse sentiment, emotion, and behavioural patterns derived from tourism-related social data and immersive interaction environments. The review aims to construct a taxonomy of RNN architectures, map their functional roles in social listening pipelines, and evaluate model performance, validation practices, and integration strategies with VR systems. Particular attention is directed towards decision-support implications for destination management, experience design, and innovation governance in smart tourism ecosystems. By adhering to PRISMA 2020 guidelines and preregistered methodological procedures, this study contributes to methodological transparency, reproducibility, and conceptual consolidation in the emerging field of AI-driven immersive social analytics. The findings are expected to inform researchers, system developers, and policy stakeholders on robust and responsible design pathways for VR-enabled social listening in smart tourism.