Abstract
Outdoor air pollution is a serious problem in many developing countries today. This study focuses on monitoring the dynamic changes of air quality effectively in large cities by analyzing the spatiotemporal trends in geo-targeted social media messages with comprehensive big data filtering procedures. We introduce a new social media analytic framework to (1) investigate the relationship between air pollution topics posted in Sina Weibo (Chinese Twitter) and the daily Air Quality Index (AQI) published by China’s Ministry of Environmental Protection; and (2) monitor the dynamics of air quality index by using social media messages. Correlation analysis was used to compare the connections between discussion trends in social media messages and the temporal changes in the AQI during 2012. We categorized relevant messages into three types, retweets, mobile app messages, and original individual messages finding that original individual messages had the highest correlation to the Air Quality Index. Based on this correlation analysis, individual messages were used to monitor the AQI in 2013. Our study indicates that the filtered social media messages are strongly correlated to the AQI and can be used to monitor the air quality dynamics to some extent.
Highlights
In recent years, social media, such as Twitter, Facebook, and Sina Weibo, has become a major communication channel in our society [1, 2]
“问题”, “污染物”, “环境” and “帝都” were descriptions of air pollution in Beijing. “口 罩” referred to protective gear while “肺癌” suggested popular awareness of the health risks associated with air pollution
We explored the relationship between the temporal trends in individual messages and the dynamic changes of the daily Air Quality Index (AQI) by using the Pearson correlation coefficient
Summary
Social media, such as Twitter, Facebook, and Sina Weibo (a popular Chinese version of social media synonymous to Twitter), has become a major communication channel in our society [1, 2]. In Japan, researchers found that a sudden increase in microblogging activities could be used to detect earthquake tremors in real-time [11, 12] These studies indicate that social media messages in the cyberspace can be used to explore the issue in the real world. Users can be considered as “social sensors” [4] who post air pollution related messages on social media platforms; rich information about popular perceptions of air quality in the real world can be extracted from these messages. We introduce a new geo-targeted social media analytic method to (1) investigate the dynamic relationship between air pollution-related posts on Sina Weibo and daily AQI values; (2) apply Gradient Tree Boosting, a machine learning method, to monitor the dynamics of AQI using filtered social media messages. Our results expose the spatiotemporal relationships between social media messages and real-world environmental changes as well suggesting new ways to monitor air pollution using social media
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