Articlegreen2022

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

Nina Martin · Lisa Poirier · Andrew J. Rosenblum · Melissa M. Reznar · Joel Gittelsohn · Daniel J. Barnett

10 citations0/0 files storedDisaster Medicine and Public Health Preparedness

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Public Relations and Crisis Communication Food Security and Socioeconomic Dynamics Food Security and Health in Diverse Populations

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.

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2020The Impact of COVID-19 on Health Behavior, Stress, Financial and Food Security among Middle to High Income Canadian Families with Young Children 2020Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter 2020Impacts of COVID-19 on Trade and Economic Aspects of Food Security: Evidence from 45 Developing Countries 2021Twitter for disaster relief through sentiment analysis for COVID-19 and natural hazard crises 2019Extracting Resource Needs and Availabilities From Microblogs for Aiding Post-Disaster Relief Operations 2021Multi-modal classification of Twitter data during disasters for humanitarian response 2019Detecting Informative Tweets during Disaster using Deep Neural Networks 2020Classifying informative and non-informative tweets from the twitter by adapting image features during disaster

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