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Effects of PM 2.5 on People’s Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing

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  • Siqing Shan

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

  • Xijie Ju

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

  • Yigang Wei

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

  • Zijin Wang

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

Abstract

PM 2.5 not only harms physical health but also has negative impacts on the public’s wellbeing and cognitive and behavioral patterns. However, traditional air quality assessments may fail to provide comprehensive, real-time monitoring of air quality because of the sparse distribution of air quality monitoring stations. Overcoming some key limitations of traditional surface monitoring data, Web-based social media platforms, such as Twitter, Weibo, and Facebook, provide a promising tool and novel perspective for environmental monitoring, prediction, and evaluation. This study aims to investigate the relationship between PM 2.5 levels and people’s emotional intensity by observing social media postings. This study defines the “emotional intensity” indicator, which is measured by the number of negative posts on Weibo, based on Weibo data related to haze from 2016 and 2017. This study estimates sentiment polarity using a recurrent neural networks model based on LSTM (Long Short-Term Memory) and verifies the correlation between high PM 2.5 levels and negative posts on Weibo using a Pearson correlation coefficient and multiple linear regression model. This study makes the following observations: (1) Taking the two-year data as an example, this study recorded the significant influence of PM 2.5 levels on netizens’ posting behavior. (2) Air quality, meteorological factors, the seasons, and other factors have a strong influence on netizens’ emotional intensity. (3) From a quantitative viewpoint, the level of PM 2.5 varies by 1 unit, and the number of negative Weibo posts fluctuates by 1.0168 units. Thus, it can be concluded that netizens’ emotional intensity is significantly positively affected by levels of PM 2.5 . The high correlation between PM 2.5 levels and emotional intensity and the sensitivity of social media data shows that social media data can be used to provide a new perspective on the assessment of air quality.

Suggested Citation

  • Siqing Shan & Xijie Ju & Yigang Wei & Zijin Wang, 2021. "Effects of PM 2.5 on People’s Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing," IJERPH, MDPI, vol. 18(10), pages 1-21, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5422-:d:557620
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    Cited by:

    1. Yang Liu & Mei-Po Kwan & Zihan Kan, 2023. "Inconsistent Association between Perceived Air Quality and Self-Reported Respiratory Symptoms: A Pilot Study and Implications for Environmental Health Studies," IJERPH, MDPI, vol. 20(2), pages 1-17, January.
    2. Siqing Shan & Xijie Ju & Yigang Wei & Xin Wen, 2022. "Concerned or Apathetic? Using Social Media Platform (Twitter) to Gauge the Public Awareness about Wildlife Conservation: A Case Study of the Illegal Rhino Trade," IJERPH, MDPI, vol. 19(11), pages 1-21, June.
    3. Xuan Tian & Cheng Zhang & Bing Xu, 2022. "The Impact of Air Pollution on Residents’ Happiness: A Study on the Moderating Effect Based on Pollution Sensitivity," IJERPH, MDPI, vol. 19(12), pages 1-20, June.
    4. Guo, Minjia & Zong, Xiangyu & Guo, Lubingzhi & Lei, Yongyu, 2024. "Does haze-related sentiment affect income inequality in China?," International Review of Economics & Finance, Elsevier, vol. 94(C).

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