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Decoding public sentiment on pension policies in China through natural language processing

Author

Listed:
  • Xiaohong Xie

    (Department of Econometrics and Statistics, Nicolaus Copernicus University in Torun)

  • Magdalena Osińska

    (Department of Economics, Nicolaus Copernicus University in Torun;)

Abstract

This study aims to reveal public sentiment toward China’s pension policies from January 2018 to August 2023, leveraging over 260,000 Weibo posts to identify key themes and demographic differences. Advanced Natural Language Processing (NLP) techniques, including sentiment analysis and latent Dirichlet allocation, are employed to explore six topics, such as societal impact and policy integrity, while uncovering demographic and regional variations. The findings reveal that policy changes significantly influence public sentiment, with greater negativity observed around institutional and structural aspects of the policies. These results underscore the need for public education on pension reforms and fraud prevention, providing actionable insights for policymakers in an aging society. The study contributes to behavioural finance theory by illustrating how heuristics like availability bias and loss aversion shape public reactions to pension reforms. However, social media data may not fully represent less active groups like older adults, highlighting the need for broader research methods

Suggested Citation

  • Xiaohong Xie & Magdalena Osińska, 2025. "Decoding public sentiment on pension policies in China through natural language processing," Bank i Kredyt, Narodowy Bank Polski, vol. 56(5), pages 613-642.
    • Handle: RePEc:nbp:nbpbik:v:56:y:2025:i:5:p:613-642
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions
    • J18 - Labor and Demographic Economics - - Demographic Economics - - - Public Policy
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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