Monitoring Environmental Quality by Sniffing Social Media

Zhibo Wang, Lei Ke, Xiaohui Cui, Qi Yin, Longfei Liao, Lu Gao and Zhenyu Wang
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Zhibo Wang: International School of Software, Wuhan University, Wuhan 430079, China
Lei Ke: International School of Software, Wuhan University, Wuhan 430079, China
Xiaohui Cui: International School of Software, Wuhan University, Wuhan 430079, China
Qi Yin: International School of Software, Wuhan University, Wuhan 430079, China
Longfei Liao: International School of Software, Wuhan University, Wuhan 430079, China
Lu Gao: International School of Software, Wuhan University, Wuhan 430079, China
Zhenyu Wang: International School of Software, Wuhan University, Wuhan 430079, China

Sustainability, 2017, vol. 9, issue 2, 1-14

Abstract: Nowadays, the environmental pollution and degradation in China has become a serious problem with the rapid development of Chinese heavy industry and increased energy generation. With sustainable development being the key to solving these problems, it is necessary to develop proper techniques for monitoring environmental quality. Compared to traditional environment monitoring methods utilizing expensive and complex instruments, we recognized that social media analysis is an efficient and feasible alternative to achieve this goal with the phenomenon that a growing number of people post their comments and feelings about their living environment on social media, such as blogs and personal websites. In this paper, we self-defined a term called the Environmental Quality Index (EQI) to measure and represent people’s overall attitude and sentiment towards an area’s environmental quality at a specific time; it includes not only metrics for water and food quality but also people’s feelings about air pollution. In the experiment, a high sentiment analysis and classification precision of 85.67% was obtained utilizing the support vector machine algorithm, and we calculated and analyzed the EQI for 27 provinces in China using the text data related to the environment from the Chinese Sina micro-blog and Baidu Tieba collected from January 2015 to June 2016. By comparing our results to with the data from the Chinese Academy of Sciences (CAS), we showed that the environment evaluation model we constructed and the method we proposed are feasible and effective.

Keywords: social media; environmental quality; environment monitoring; Support Vector Machine (SVM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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