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. 2021 May 19;18(10):5422.
doi: 10.3390/ijerph18105422.

Effects of PM2.5 on People's Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing

Siqing Shan  1   2 , Xijie Ju  1   2 , Yigang Wei  1   2 , Zijin Wang  1   2
Affiliations

Effects of PM2.5 on People's Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing

Siqing Shan et al. Int J Environ Res Public Health. .

Abstract

PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5. The high correlation between PM2.5 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.

Keywords: PM2.5; machine learning; sentiment analysis; social media data.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Distribution map of cities with national air monitoring sites (2018).
Figure 2
Figure 2
Analytical framework for using social media data for PM2.5 research.
Figure 3
Figure 3
Implementation of the SVM classifier.
Figure 4
Figure 4
Diagram of the LSTM model.
Figure 5
Figure 5
The number of negative microblogs and concentration levels of PM2.5.
Figure 6
Figure 6
The trend of negative microblogs and concentration levels of PM2.5 for every month during 2016 and 2017.
Figure 6
Figure 6
The trend of negative microblogs and concentration levels of PM2.5 for every month during 2016 and 2017.
Figure 7
Figure 7
The trend of negative microblogs and concentration levels of PM2.5 in August 2016.
See this image and copyright information in PMC

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