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Artificial intelligence (AI) monitored Twitter to track food security discussions during the COVID-19 pandemic. AI identified increased public concern about food needs, economic crises, and essential services, highlighting its value in crisis response.

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Area of Science:

  • Computational social science
  • Public health informatics
  • Artificial intelligence in social sciences

Background:

  • Food security is critical during public health emergencies, necessitating robust situational awareness of needs and resources.
  • Artificial intelligence (AI) and machine learning (ML) offer advanced capabilities for crisis situational awareness and resource allocation.
  • Limited research has explored AI-driven monitoring of food security discourse on social media during the COVID-19 pandemic.

Purpose of the Study:

  • To address the research gap by employing AI to classify food security topics on Twitter.
  • To analyze the daily frequency of these food security topics during the COVID-19 pandemic.
  • To assess the utility of AI in understanding public discourse surrounding food security during crises.

Main Methods:

  • Scraping 237,107 tweets from Twitter between January 2020 and December 2021 using food security keywords.
  • Applying Latent Dirichlet Allocation (LDA) for topic modeling to classify tweet content.
  • Conducting time-series analyses to examine the daily frequency of identified topics.

Main Results:

  • Tweets were classified into key topics: food needs/resources, emergency preparedness/response, and mental/physical health.
  • A relative increase in daily topic density was observed for food pantries, food banks, economic/food security crises, essential services, and preparedness advice following the WHO pandemic declaration.
  • Emerging food security threats in Tigray were identified in 2021.

Conclusions:

  • AI presents a powerful, underutilized tool for monitoring food insecurity via social media.
  • Prioritizing ML tools for enhancing emergency response and measuring their impact is recommended.
  • Further research involving supervised ML models and validated food insecurity word patterns can accelerate AI adoption by policymakers and aid organizations.