Real-time Nowcasting of Food Insecurity with Google Trends Data

Real-time Nowcasting of Food Insecurity with Google Trends Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Real-time Nowcasting of Food Insecurity with Google Trends Data Nicola Caravaggio, Bia Carneiro, Giuliano Resce This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8095652/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This research explores the potential of Google Trends (GT) as a tool for generating a daily index of food insecurity at the national level, focusing on countries monitored by the Famine Early Warning Systems Network (FEWS NET) and the U.S. Global Fragility Act (GFA). Drawing inspiration from previous studies on GT’s predictive capabilities, the authors employ Natural Language Processing (NLP) to analyze FEWS NET’s food security reporting and identify key predictors of food insecurity using a LASSO regression approach. The predictors are then queried on GT to construct a daily sentiment index for each country. Unlike other approaches, the study considers multiple languages and weighs search terms based on LASSO coefficients. The resulting Synthetic Search Interest (SSI) Index for food insecurity demonstrates a statistically significant correlation with the UN’s Food and Agriculture Organization’s (FAO) share of the population in severe food insecurity, affirming GT’s potential as a monitoring tool. This research introduces a novel methodology and valuable insights for leveraging real-time data to enhance early warning systems in food security. JEL Classification: C53, C82, Q18. Physical sciences/Mathematics and computing Social science/Science technology and society Food Security Google Trends Early Warning Nowcasting Natural Language Processing. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Nov, 2025 Editor assigned by journal 12 Nov, 2025 Submission checks completed at journal 12 Nov, 2025 First submitted to journal 12 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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