Conceptualizing Social Media Perceptions of Spatial Planning Policy in China: Thematic Concerns, Sentiment Dynamics, and Implementation Feedback

Social media provides insights into how social media users perceive and evaluate complex public policies, yet its application to spatial planning policy research remains limited. This study examines perceptions of China’s territorial spatial planning (TSP) reform using 17,945 posts and comments collected from Weibo, Zhihu, and Redbook between January 2022 and July 2026, of which 12,767 were retained after relevance screening. Combining latent Dirichlet allocation (LDA), bidirectional encoder representations from transformers (BERT), text network analysis (TNA) and qualitative coding, the study identifies three findings. (1) LDA identifies three topics in which TSP policy is understood: spatial development strategy and protection (22.4%), urban positioning and spatial structure (35.2%), and plan preparation, approval, and implementation management (42.4%). (2) BERT-based sentiment classification suggests that negative sentiment predominates in the analysed corpus (56.1%), especially on Redbook (75.6%), overall sentiment scores fell in 2024 and subsequently stabilised. (3) TNA and qualitative coding of negative records identified 17 categories of perceived implementation concerns, including spatial regulation and local needs, development opportunities and infrastructure, and planning procedures and coordination. By applying a mixed computational and qualitative approach to large-scale social media discussions concerning an institutionally complex planning reform, the study identifies implementation-related issues that warrant further investigation.

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Publication Details

Journal
Land
Published
2026-10-26
DOI
https://doi.org/10.3390/land15112032
Primary Topic
Local Governance and Planning
Type
article
Field-Weighted Citation Impact
0.00

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article

Conceptualizing Social Media Perceptions of Spatial Planning Policy in China: Thematic Concerns, Sentiment Dynamics, and Implementation Feedback

Nannan Zhao, Jianyun Zhou, Yawen Zhou, Ruishi Wang
Land
Local Governance and Planning
article

Conceptualizing Social Media Perceptions of Spatial Planning Policy in China: Thematic Concerns, Sentiment Dynamics, and Implementation Feedback

Nannan Zhao, Jianyun Zhou, Yawen Zhou, Ruishi Wang
article en

Abstract

Social media provides insights into how social media users perceive and evaluate complex public policies, yet its application to spatial planning policy research remains limited. This study examines perceptions of China’s territorial spatial planning (TSP) reform using 17,945 posts and comments collected from Weibo, Zhihu, and Redbook between January 2022 and July 2026, of which 12,767 were retained after relevance screening. Combining latent Dirichlet allocation (LDA), bidirectional encoder representations from transformers (BERT), text network analysis (TNA) and qualitative coding, the study identifies three findings. (1) LDA identifies three topics in which TSP policy is understood: spatial development strategy and protection (22.4%), urban positioning and spatial structure (35.2%), and plan preparation, approval, and implementation management (42.4%). (2) BERT-based sentiment classification suggests that negative sentiment predominates in the analysed corpus (56.1%), especially on Redbook (75.6%), overall sentiment scores fell in 2024 and subsequently stabilised. (3) TNA and qualitative coding of negative records identified 17 categories of perceived implementation concerns, including spatial regulation and local needs, development opportunities and infrastructure, and planning procedures and coordination. By applying a mixed computational and qualitative approach to large-scale social media discussions concerning an institutionally complex planning reform, the study identifies implementation-related issues that warrant further investigation.

LandVol. 15(11)
South China Normal University (CN), Guangzhou Urban Planning Survey & Design Institute (CN), South China University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 3%
Local Governance and Planning
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