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Accepted manuscript

Enhancing Artificial Intelligence for Twitter-based Public Discourse on Food Security During the COVID-19 Pandemic

Published online by Cambridge University Press:  04 August 2022

Nina M. Martin Open the ORCID record for Nina M. Martin [Opens in a new window] ,
Lisa Poirier ,
Andrew J. Rosenblum Open the ORCID record for Andrew J. Rosenblum [Opens in a new window] ,
Melissa M. Reznar ,
Joel Gittelsohn  and
Daniel J. Barnett
Nina M. Martin*
Affiliation:
Assistant Scientist, Johns Hopkins Bloomberg School of Public Health, Department of International Health, Human Nutrition Program, Address: 615 N. Wolfe St., Baltimore, MD, 21205, Email : [email protected]
Lisa Poirier
Affiliation:
Research Associate, Johns Hopkins Bloomberg School of Public Health, Department of International Health, 615 N Wolfe St, Baltimore, MD, 21205, Email: [email protected]
Andrew J. Rosenblum
Affiliation:
Law Student, American University Washington College of Law, 4300 Nebraska Ave. NW, Washington DC 20016 Part Time Lecturer, Krieger School of Arts & Sciences, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218, Email: [email protected]
Melissa M. Reznar
Affiliation:
Associate Professor, Oakland University School of Health Sciences, 433 Meadow Brook Road Rochester, MI 48309, Phone : 248-364-8668, Email: [email protected]
Joel Gittelsohn
Affiliation:
Professor, Johns Hopkins Bloomberg School of Public Health, Department of International Health, Center for Human Nutrition, 615 N Wolfe St, Baltimore, MD, 21205. Phone: 410-955-3927 Email: [email protected]
Daniel J. Barnett
Affiliation:
Associate Professor, Johns Hopkins Bloomberg School of Public Health, Department of Environmental Health and Engineering, 615 N. Wolfe Street, Room E7036, Baltimore, Maryland 21205, Phone: 410-502-0591, Fax: 410-955-0617, Email: [email protected]
*
*Corresponding author: Email : [email protected]
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Abstract

Objective:

Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day.

Methods:

Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.

Results:

237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.

Conclusions:

AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

Keywords

Food SecurityMachine LearningTopic ModelingTwitterNatural Language Processing
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Accepted manuscript , pp. 1 - 25
Copyright
© 2022 Society for Disaster Medicine and Public Health, Inc.

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