Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform

Kui Liu, Li Li, Tao Jiang, Bin Chen, Zhenggang Jiang, Zhengting Wang, Yongdi Chen, Jianmin Jiang and Hua Gu
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Kui Liu: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
Li Li: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
Tao Jiang: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
Bin Chen: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
Zhenggang Jiang: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
Zhengting Wang: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
Yongdi Chen: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
Jianmin Jiang: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
Hua Gu: Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China

IJERPH, 2016, vol. 13, issue 8, 1-15

Abstract: Objective : The outbreak of the Ebola epidemic in West Africa in 2014 exerted enormous global public reaction via the Internet and social media. This study aimed to investigate and evaluate the public reaction to Ebola in China and identify the primitive correlation between possible influence factors caused by the outbreak of Ebola in West Africa and Chinese public attention via Internet surveillance. Methods : Baidu Index (BDI) and Sina Micro Index (SMI) were collected from their official websites, and the disease-related data were recorded from the websites of the World Health Organization (WHO), U.S. Centers for Disease Control and Prevention (CDC), and U.S. National Ministries of Health. The average BDI of Internet users in different regions were calculated to identify the public reaction to the Ebola outbreak. Spearman’s rank correlation was used to check the relationship of epidemic trends with BDI and SMI. Additionally, spatio-temporal analysis and autocorrelation analysis were performed to detect the clustered areas with the high attention to the topic of “Ebola”. The related news reports were collected from authoritative websites to identify potential patterns. Results : The BDI and the SMI for “Ebola” showed a similar fluctuating trend with a correlation coefficient = 0.9 ( p < 0.05). The average BDI in Beijing, Tibet, and Shanghai was higher than other cities. However, the disease-related indicators did not identify potential correlation with both indices above. A hotspot area was detected in Tibet by local autocorrelation analysis. The most likely cluster identified by spatiotemporal cluster analysis was in the northeast regions of China with the relative risk (RR) of 2.26 ( p ? 0.01) from 30 July to 14 August in 2014. Qualitative analysis indicated that negative news could lead to a continuous increase of the public’s attention until the appearance of a positive news report. Conclusions : Confronted with the risk of cross-border transmission of the infectious disease, online surveillance might be used as an innovative approach to perform public communication and health education through examining the public’s reaction and attitude.

Keywords: Ebola; Internet surveillance; public attention; Big Data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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