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Analyzing public demands on China’s online government inquiry platform: A BERTopic-Based topic modeling study

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  • Zhuoyuan Tang
  • Xuan Pan
  • Zhouyi Gu

Abstract

This study aims to enhance governmental decision-making by leveraging advanced topic modeling algorithms to analyze public letters on the "People Call Me" online government inquiry platform in Zhejiang Province, China. Employing advanced web scraping techniques, we collected publicly available letter data from Hangzhou City between June 2022 and May 2023. Initial descriptive statistical analyses and text mining were conducted, followed by topic modeling using the BERTopic algorithm. Our findings indicate that public demands are chiefly focused on livelihood security and rights protection, and these demands exhibit a diversity of characteristics. Furthermore, the public’s response to significant emergency events demonstrates both sensitivity and deep concern, underlining its pivotal role in government emergency management. This research not only provides a comprehensive landscape of public demands but also validates the efficacy of the BERTopic algorithm for extracting such demands, thereby offering valuable insights to bolster the government’s agility and resilience in emergency responses, enhance public services, and modernize social governance.

Suggested Citation

  • Zhuoyuan Tang & Xuan Pan & Zhouyi Gu, 2024. "Analyzing public demands on China’s online government inquiry platform: A BERTopic-Based topic modeling study," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-26, February.
    • Handle: RePEc:plo:pone00:0296855
    DOI: 10.1371/journal.pone.0296855
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    References listed on IDEAS

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    1. Shaoming Cheng & Sukumar Ganapati & Giri Narasimhan & Farzana Beente Yusuf, 2022. "A machine learning‐based analysis of 311 requests in the Miami‐Dade County," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1627-1645, December.
    2. Berliner, Daniel & Bagozzi, Benjamin E. & Palmer-Rubin, Brian, 2018. "What information do citizens want? Evidence from one million information requests in Mexico," World Development, Elsevier, vol. 109(C), pages 222-235.
    3. Supunmali Ahangama, 2023. "Relating Social Media Diffusion, Education Level and Cybersecurity Protection Mechanisms to E-Participation Initiatives: Insights from a Cross-Country Analysis," Information Systems Frontiers, Springer, vol. 25(5), pages 1695-1711, October.
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