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Open AccessCase Report

Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis

1
Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA
2
Computational Epidemiology Lab, Harvard Medical School, Boston, MA 02215, USA
3
Innovation Program, Boston Children’s Hospital, Boston, MA 02215, USA
4
Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
5
Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA
6
Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(14), 5077; https://doi.org/10.3390/ijerph17145077
Received: 30 May 2020 / Revised: 2 July 2020 / Accepted: 7 July 2020 / Published: 14 July 2020
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
Globally, water scarcity has become a common challenge across many regions. Digital surveillance holds promise for monitoring environmental threats to population health due to severe drought. The 2019 Chennai water crisis in India resulted in severe disruptions to social order and daily life, with local residents suffering due to water shortages. This case study explored public opinion captured through the Twitter social media platform, and whether this information could help local governments with emergency response. Sentiment analysis and topic modeling were used to explore public opinion through Twitter during the 2019 Chennai water crisis. The latent Dirichlet allocation (LDA) method identified topics that were most frequently discussed. A naïve Tweet classification method was built, and Twitter posts (called tweets) were allocated to identified topics. Topics were ranked, and corresponding emotions were calculated. A cross-correlation was performed to examine the relationship between online posts about the water crisis and actual rainfall, determined by precipitation levels. During the Chennai water crisis, Twitter users posted content that appeared to show anxiety about the impact of the drought, and also expressed concerns about the government response. Twitter users also mentioned causes for the drought and potential sustainable solutions, which appeared to be mainly positive in tone. Discussion on Twitter can reflect popular public opinion related to emerging environmental health threats. Twitter posts appear viable for informing crisis management as real-time data can be collected and analyzed. Governments and public health officials should adjust their policies and public communication by leveraging online data sources, which could inform disaster prevention measures. View Full-Text
Keywords: digital surveillance; disasters; crisis; water; public opinion; social media; natural language processing digital surveillance; disasters; crisis; water; public opinion; social media; natural language processing
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MDPI and ACS Style

Xiong, J.; Hswen, Y.; Naslund, J.A. Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis. Int. J. Environ. Res. Public Health 2020, 17, 5077. https://doi.org/10.3390/ijerph17145077

AMA Style

Xiong J, Hswen Y, Naslund JA. Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis. International Journal of Environmental Research and Public Health. 2020; 17(14):5077. https://doi.org/10.3390/ijerph17145077

Chicago/Turabian Style

Xiong, Jiangmei; Hswen, Yulin; Naslund, John A. 2020. "Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis" Int. J. Environ. Res. Public Health 17, no. 14: 5077. https://doi.org/10.3390/ijerph17145077

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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