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Exploring the Mental Health Benefits of Urban Green Spaces Through Social Media Big Data: A Case Study of the Changsha–Zhuzhou–Xiangtan Urban Agglomeration

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  • Zhijian Li

    (School of Architecture and Art, Central South University, Changsha 410075, China)

  • Tian Dong

    (School of Architecture and Art, Central South University, Changsha 410075, China)

Abstract

Urban green spaces (UGSs) provide recreational and cultural services to urban residents and play an important role in mental health. This study uses big data mining techniques to analyze 62 urban parks in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA) based on data such as points of interest (POIs), areas of interest (AOIs), and user comments from the popular social media platform Dianping. In addition, the authors apply sentiment analysis using perceptual dictionaries combined with geographic information data to identify text emotions. A structural equation model (SEM) was constructed in IBM SPSS AMOS 24.0 software to investigate the relationship between five external features, five types of cultural services, nine landscape elements, four environmental factors, and tourist emotions. The results show that UGS external features, cultural services, landscape elements, and environmental factors all have positive effects on residents’ emotions, with landscape elements having the greatest impact. The other factors show similar effects on residents’ moods. In various UGSs, natural elements such as vegetation and water tend to evoke positive emotions in residents, while artificial elements such as roads, squares, and buildings elicit more varied emotional responses. This research provides science-based support for the design and management of urban parks.

Suggested Citation

  • Zhijian Li & Tian Dong, 2025. "Exploring the Mental Health Benefits of Urban Green Spaces Through Social Media Big Data: A Case Study of the Changsha–Zhuzhou–Xiangtan Urban Agglomeration," Sustainability, MDPI, vol. 17(8), pages 1-26, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3465-:d:1633892
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