Unraveling the nexus between spatial quality and buzz behavior: Analyzing geo-tagged social media and multisource spatial data using text mining and XGBoost

In the digital media era, geo-tagged social media (SM) has emerged as a powerful tool for understanding tourist behavior and managing destination image. This study examines how spatial quality influences buzz behavior—rapid, user-driven dissemination of destination information—through the lens of the Feedback, Sympathy, Identification, Participation, and Sharing (FSIPS) framework. Focusing on Suzhou's historic city core, we integrate ridge regression, deep learning-based text mining, and eXtreme Gradient Boosting (XGBoost) to model the relationship between built environment features and SM engagement metrics, including emotional expression and likes. Findings reveal that (1) buzz behavior, driven by emotionally resonant user-generated content, plays a critical role in shaping destination popularity; (2) spatial features such as park and catering density significantly enhance emotional responses and content diffusion; and (3) entertainment density shows a negative association with engagement, suggesting diminishing returns in over-commercialized zones. (4) Furthermore, the analysis uncovers non-linear interaction effects—e.g., the co-presence of green infrastructure and public transport density synergistically boosts perceptual response. This research contributes a theoretically grounded and data-driven framework for decoding the spatial triggers of word-of-mouth dynamics in tourism, offering actionable insights for planners aiming to enhance tourist experiences and manage spatial quality in heritage-rich urban settings.