Version 1
: Received: 11 July 2022 / Approved: 13 July 2022 / Online: 13 July 2022 (09:14:39 CEST)
How to cite:
Martin, N.M.; Poirier, L.; Rosenblum, A.J.; Reznar, M.M.; Gittelsohn, J.; Barnett, D.J. Enhancing Artificial Intelligence for Twitter-based Public Discourse on Food Security During the COVID-19 Pandemic. Preprints2022, 2022070196. https://doi.org/10.20944/preprints202207.0196.v1.
Martin, N.M.; Poirier, L.; Rosenblum, A.J.; Reznar, M.M.; Gittelsohn, J.; Barnett, D.J. Enhancing Artificial Intelligence for Twitter-based Public Discourse on Food Security During the COVID-19 Pandemic. Preprints 2022, 2022070196. https://doi.org/10.20944/preprints202207.0196.v1.
Cite as:
Martin, N.M.; Poirier, L.; Rosenblum, A.J.; Reznar, M.M.; Gittelsohn, J.; Barnett, D.J. Enhancing Artificial Intelligence for Twitter-based Public Discourse on Food Security During the COVID-19 Pandemic. Preprints2022, 2022070196. https://doi.org/10.20944/preprints202207.0196.v1.
Martin, N.M.; Poirier, L.; Rosenblum, A.J.; Reznar, M.M.; Gittelsohn, J.; Barnett, D.J. Enhancing Artificial Intelligence for Twitter-based Public Discourse on Food Security During the COVID-19 Pandemic. Preprints 2022, 2022070196. https://doi.org/10.20944/preprints202207.0196.v1.
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 Security; Machine Learning; Topic Modeling; Twitter; Natural Language Processing
Subject
MEDICINE & PHARMACOLOGY, Nutrition
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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