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Understanding public attitudes towards progressive delayed retirement policy in China

Evidence from social media topic modelling and sentiment analysis

Bibliographic Data

ID19480331
AuthorsRenxing Chen (0000-0002-7634-5908, Shandong University), Ruzhe Geng (Shandong University), Yuxiang Duan (0000-0003-4319-7627, Shandong University), Wenjuan Wang (0000-0001-9180-2823, University of Jinan, corresponding author)
Year2026
Pages1-20
Publication date2026-05-12
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Asian Public Policy (JOURNAL)
Journal identifiersISSN: 1751-6242 • E-ISSN: 1751-6234
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/17516234.2026.2671818
OpenAlexW7160961342
LanguageEN
References cited67

With China’s rapidly ageing population, the recently introduced progressive delayed retirement policy has attracted significant public attention and generated widespread debate. This study investigates public responses during the early implementation phase of the reform. Based on 10,788 Sina Weibo posts collected between September 2024 and March 2025, sentiment analysis and Latent Dirichlet Allocation (LDA) topic modelling were employed to trace the dynamics of online discourse. The analysis revealed six major themes: policy suggestions, policy details, policy impacts, policy implementation, policy interpretation, and policy announcement. Prominent concerns focused on employment competition, retirement-age thresholds, and pension contribution and distribution arrangements. Sentiment analysis indicated a notable shift in public emotions – from initial anxiety and scepticism to gradual acceptance – as policy communication and interpretation became more comprehensive. Overall, the findings show that changes in issue salience and emotional attitudes serve as early indicators of public feedback, influencing perceptions of policy legitimacy and underscoring the need for more flexible retirement options, complementary measures, and improved policy communication

Public opinion · Public policy · Sentiment analysis · Social media · Computational and Text Analysis Methods · Mental Health via Writing · Sentiment Analysis and Opinion Mining

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