IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v12y2025i1d10.1057_s41599-025-04879-9.html
   My bibliography  Save this article

Monitoring public sentiment and sensitivity to air pollution in China: a text mining approach on Sina Weibo

Author

Listed:
  • Binbin Ye

    (Guangdong University of Finance & Economics)

  • Shiguo Jia

    (Sun Yat-sen University
    Sun Yat-sen University)

Abstract

Public response (including sentiment and sensitivity) to air pollution, one of the most serious health threats in recent years, can be monitored in real-time across China through social media channels. However, few studies have long-term investigations into both public sentiment and sensitivity towards different air pollutants while considering socioeconomic factors, resulting in an incomplete understanding of public reactions to air pollution and less effective policy measures. In this paper, we employed sentiment analysis to classify Weibos with positive and negative sentiments, then explored the relationship between the concentrations of six air pollutants (PM2.5, PM10, CO, NO2, O3, and SO2) and Weibos related to air pollution during 2017–2021 across China. The results show that residents in China exhibit the greatest sensitivity and express the most negative sentiments toward PM2.5, both at the national level and for individual provinces. After filtering out positive Weibos by sentiment analysis, there would be a stronger relationship between the number of negative Weibos reflecting public sentiment about air pollution and PM2.5 concentrations. A threshold effect has been identified where public reaction plateaus or wanes at high pollution levels. Socioeconomic factors, including education level, economic conditions, and network usage, are found to influence public sentiment towards air pollution. This study highlights the critical role of targeted policy interventions and the application of sentiment analysis in effectively understanding and addressing public concerns about air pollution, particularly PM2.5, which is essential for enhancing environmental health strategies across China.

Suggested Citation

  • Binbin Ye & Shiguo Jia, 2025. "Monitoring public sentiment and sensitivity to air pollution in China: a text mining approach on Sina Weibo," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04879-9
    DOI: 10.1057/s41599-025-04879-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-025-04879-9
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-025-04879-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Bangren & Zheng, Xinqi & Liu, Haiyan & Li, Jiayang & Wang, Peipei, 2020. "Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Siqi Zheng & Jianghao Wang & Cong Sun & Xiaonan Zhang & Matthew E. Kahn, 2019. "Air pollution lowers Chinese urbanites’ expressed happiness on social media," Nature Human Behaviour, Nature, vol. 3(3), pages 237-243, March.
    3. Zhi Cao & Jingbo Zhou & Meng Li & Jizhou Huang & Dejing Dou, 2023. "Urbanites’ mental health undermined by air pollution," Nature Sustainability, Nature, vol. 6(4), pages 470-478, April.
    4. Binbin Ye & Padmaja Krishnan & Shiguo Jia, 2022. "Public Concern about Air Pollution and Related Health Outcomes on Social Media in China: An Analysis of Data from Sina Weibo (Chinese Twitter) and Air Monitoring Stations," IJERPH, MDPI, vol. 19(23), pages 1-21, December.
    5. Kazutoshi Sasahara & Yoshito Hirata & Masashi Toyoda & Masaru Kitsuregawa & Kazuyuki Aihara, 2013. "Quantifying Collective Attention from Tweet Stream," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
    6. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhenhua Zheng & Linquan Chen & Ning Sun & Yilin Jin & Yuetong Wang, 2024. "Pollution, hazards, and health inequalities: a longitudinal exploration of the impact of PM2.5 on depression among rural older adults with different incomes in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    2. Du, Rui & Mino, Ajkel & Wang, Jianghao & Zheng, Siqi, 2024. "Transboundary vegetation fire smoke and expressed sentiment: Evidence from Twitter," Journal of Environmental Economics and Management, Elsevier, vol. 124(C).
    3. Wu, Jiayi & Lai, Aolin & Li, Zhenran & Wang, Qunwei, 2024. "Investment efficiency of renewable energy enterprises when exposed to air pollution: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 96(PC).
    4. Gu, Chen & Guo, Xu & Zhang, Chengping, 2022. "Analyst target price revisions and institutional herding," International Review of Financial Analysis, Elsevier, vol. 82(C).
    5. Ruomeng Cui & Dennis J. Zhang & Achal Bassamboo, 2019. "Learning from Inventory Availability Information: Evidence from Field Experiments on Amazon," Management Science, INFORMS, vol. 65(3), pages 1216-1235, March.
    6. Jonas Hedlund & Carlos Oyarzun, 2018. "Imitation in heterogeneous populations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(4), pages 937-973, June.
    7. Cao, Melanie & Shi, Shouyong, 2006. "Signaling in the Internet craze of initial public offerings," Journal of Corporate Finance, Elsevier, vol. 12(4), pages 818-833, September.
    8. Wei He & Qian Wang, 2020. "The peer effect of corporate financial decisions around split share structure reform in China," Review of Financial Economics, John Wiley & Sons, vol. 38(3), pages 474-493, July.
    9. Kraemer, Carlo & Noth, Markus & Weber, Martin, 2006. "Information aggregation with costly information and random ordering: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 59(3), pages 423-432, March.
    10. Fishman, Arthur & Fishman, Ram & Gneezy, Uri, 2019. "A tale of two food stands: Observational learning in the field," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 101-108.
    11. Cavatorta, Elisa & Guarino, Antonio & Huck, Steffen, 2024. "Social learning with partial and aggregate information: Experimental evidence," Games and Economic Behavior, Elsevier, vol. 146(C), pages 292-307.
    12. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    13. Jacob K. Goeree & Leeat Yariv, 2015. "Conformity in the lab," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 15-28, July.
    14. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    15. Boğaçhan Çelen & Kyle Hyndman, 2012. "An experiment of social learning with endogenous timing," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 251-268, September.
    16. Hiroshi Kitamura, 2007. "Capacity Expansion in Markets with Intertemporal Consumption Externalities," Discussion Papers in Economics and Business 07-11, Osaka University, Graduate School of Economics.
    17. Bohl, Martin T. & Branger, Nicole & Trede, Mark, 2017. "The case for herding is stronger than you think," Journal of Banking & Finance, Elsevier, vol. 85(C), pages 30-40.
    18. B Kelsey Jack, "undated". "Market Inefficiencies and the Adoption of Agricultural Technologies in Developing Countries," CID Working Papers 50, Center for International Development at Harvard University.
    19. Yongqiang Zhao & Liwei Zhang, 2022. "An Advanced Study of Urban Emergency Medical Equipment Logistics Distribution for Different Levels of Urgency Demand," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    20. Hussinger, Katrin & Pellens, Maikel, 2019. "Guilt by association: How scientific misconduct harms prior collaborators," Research Policy, Elsevier, vol. 48(2), pages 516-530.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04879-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.