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

Author

Listed:
  • Kui Liu

    (Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
    These authors contributed equally to this work.)

  • Li Li

    (Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou 310051, Zhejiang, China
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Kui Liu & Li Li & Tao Jiang & Bin Chen & Zhenggang Jiang & Zhengting Wang & Yongdi Chen & Jianmin Jiang & Hua Gu, 2016. "Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform," IJERPH, MDPI, vol. 13(8), pages 1-15, August.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:8:p:780-:d:75305
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    References listed on IDEAS

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    1. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    2. Eui-Ki Kim & Jong Hyeon Seok & Jang Seok Oh & Hyong Woo Lee & Kyung Hyun Kim, 2013. "Use of Hangeul Twitter to Track and Predict Human Influenza Infection," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-11, July.
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    Cited by:

    1. Shaobo Zhong & Zhichen Yu & Wei Zhu, 2019. "Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China," IJERPH, MDPI, vol. 16(6), pages 1-19, March.
    2. Christine C. Ekenga & Cora-Ann McElwain & Nadav Sprague, 2018. "Examining Public Perceptions about Lead in School Drinking Water: A Mixed-Methods Analysis of Twitter Response to an Environmental Health Hazard," IJERPH, MDPI, vol. 15(1), pages 1-10, January.
    3. Yangkun Huang & Xiaoping Xu & Sini Su, 2021. "Diverging from News Media: An Exploratory Study on the Changing Dynamics between Media and Public Attention on Cancer in China from 2011–2020," IJERPH, MDPI, vol. 18(16), pages 1-13, August.

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