94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Policy and Governance
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1546136
This article is part of the Research TopicAdvancing Carbon Reduction and Pollution Control Policies Management: Theoretical, Application, and Future ImpactsView all 36 articles
The study on the dissemination of waste sorting policies on social media and the public's feedback attitudes: A text analysis based on comment data of policies in 46 key cities in China
Provisionally accepted- 1Jiangnan University, Wuxi, China
- 2South China Normal University, Guangzhou, Guangdong, China
- 3Zhijiang College, Zhejiang University of Technology, Shaoxin, Zhejiang Province, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
This study investigates the dissemination of major waste sorting policies and public feedback attitudes across 46 key Chinese cities using data from the Weibo social media platform. The research employs a Latent Dirichlet Allocation (LDA) topic model to identify and mine themes from comment texts, extracting multiple core discussion topics. The results show that although negative sentiments slightly outweighed positive sentiments in public comments, there was no significant difference in the focal points of attention between positive and negative sentiments. Negative sentiments primarily centered on policy specifics and implementation methods, with key concerns including details of policy execution and operational challenges. Cities such as Shanghai, Beijing, Nanjing, and Hangzhou exhibited higher volumes of policy-related discussions, indicating greater public engagement in these regions. Analysis of IP address distribution revealed pronounced regional concentration, particularly among residents in developed eastern coastal areas. Finally, the study proposes strategic recommendations for optimizing information dissemination on social media to enhance public willingness to participate in waste sorting initiatives.The study covers the waste sorting policies in 46 key cities across China, with data collected through the Sina Weibo platform, providing representative public feedback. Utilizing text mining techniques, including sentiment analysis and the LDA model, the study conducts an in-depth analysis of public comments, uncovering the public's cognition and attitudes towards waste sorting policies.The study identifies specific reasons for the public's dissatisfaction with waste sorting policies, providing policymakers with precious insights to improve policy design, optimize promotional strategies, and enhance public participation.The article highlights the regional concentration of waste sorting policy discussions, particularly the high level of resident engagement in the developed eastern coastal areas, offering a reference for policy dissemination.
Keywords: Waste Sorting1, social media2, Official Policy3, Text Analysis4, Public Feedback Attitudes5, Latent Dirichlet Allocation Model6
Received: 16 Dec 2024; Accepted: 08 Apr 2025.
Copyright: © 2025 Chen, Huang, Ma, Ma and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Suwei Ma, Jiangnan University, Wuxi, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.