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Challenges to Transforming Unconventional Social Media Data into Actionable Knowledge for Public Health Systems During Disasters

Published online by Cambridge University Press:  15 October 2019

Jennifer L. Chan Open the ORCID record for Jennifer L. Chan [Opens in a new window]  and
Hemant Purohit
Jennifer L. Chan*
Affiliation:
Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
Hemant Purohit
Affiliation:
Department of Information Sciences and Technology, Volgenau School of Engineering, George Mason University, Fairfax, Virginia
*
Correspondence and reprint requests to Jennifer L. Chan 211 E. Ontario Street, Suite 200, Chicago, IL 60611 (e-mail: [email protected]).
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Abstract

Every year, there are larger and more severe disasters and health organizations are struggling to respond with services to keep public health systems running. Making decisions with limited health information can negatively affect response activities and impact morbidity and mortality. An overarching challenge is getting the right health information to the right health service personnel at the right time. As responding agencies engage in social media (eg, Twitter, Facebook) to communicate with the public, new opportunities emerge to leverage this non-traditional information for improved situational awareness. Transforming these big data is dependent on computers to process and filter content for health information categories relevant to health responders. To enable a more health-focused approach to social media analysis during disasters, 2 major research challenges should be addressed: (1) advancing methodologies to extract relevant information for health services and creating dynamic knowledge bases that address both the global and US disaster contexts, and (2) expanding social media research for disaster informatics to focus on health response activities. There is a lack of attention on health-focused social media research beyond epidemiologic surveillance. Future research will require approaches that address challenges of domain-aware, including multilingual language understanding in artificial intelligence for disaster health information extraction. New research will need to focus on the primary goal of health providers, whose priority is to get the right health information to the right medical and public health service personnel at the right time.

Keywords

social mediadisastersinformation managementsituational awarenesscrisis informatics
Type
Concepts in Disaster Medicine
Information
Disaster Medicine and Public Health Preparedness , Volume 14 , Issue 3 , June 2020 , pp. 352 - 359
Copyright
Copyright © 2019 Society for Disaster Medicine and Public Health, Inc.

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