ArticleScientific reports2026
Using social media data to improve specific urban park activities: the case of park camping.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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Abstract
Emerging urban nature-based leisure practices in public parks require activity-oriented evidence that can identify experience gaps, prioritize improvement needs, and clarify feasibility boundaries under governance constraints; this study develops an interpretable workflow using urban park camping as a representative case. We collected 5449 Little Red Book posts, retained 4,840 valid posts after cleaning, and extracted 71 camping-related topics via LDA with TF-IDF weighting; keyword-level sentiment evaluation and ODI opportunity scoring were then applied to prioritize underserved topics, and semi-structured expert interviews provided cross-actor validation and feasibility clarification. Across all topic keywords, the mean sentiment score was 0.654, indicating generally positive evaluations, while negative evaluations concentrated on a limited set of high-salience elements within topics, enabling targeted diagnosis beyond theme-level summaries. Six high-opportunity topics were identified as priority areas for improvement, and integrating within-topic weight-sentiment configurations, opportunity scores, and expert evidence revealed three linked mechanisms explaining persistent gaps in park camping: misalignment between activity demand and feasible supply, scale differences between comfort-oriented environmental evaluation and cumulative ecological constraints, and conflict amplification through shortcomings in basic facilities and reservation management. Methodologically, the study operationalizes transparent prioritization by combining importance- and satisfaction-related signals and by locating dissatisfaction drivers at the keyword level; practically, it provides a structured basis for targeting peak-sensitive bottlenecks and communicating feasibility boundaries for boundary-sensitive demands in urban parks.
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