首页|Unsupervised news analysis for enhanced high-frequency food insecurity assessment

Unsupervised news analysis for enhanced high-frequency food insecurity assessment

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  • NETL
  • NSTL
  • Wiley
This article introduces an artificial intelligence (AI)-based system for forecasting food insecurity in data-limited settings, employing unsupervised neural networks for topic modeling on news data. Unlike traditional methods, our system operates without relying on expert assumptions about food insecurity factors. Through a case study in Somalia, we show that the method can yield competitive performance, even in the absence of traditional food security indicators such as food prices. This system is valuable in supporting expert assessments of food insecurity, unlocking a wealth of untapped information from news outlets, and offering a path toward more frequent and automated food insecurity monitoring for timely crisis intervention.

food insecuritynews analysisSomaliatime series forecastingunsupervised topic modeling

Cascha van Wanrooij、Frans Cruijssen、Juan Sebastian Olier

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Department of Econometrics and Operations Research,School of Economics and Management||Zero Hunger Lab,Tilburg University,Tilburg,The Netherlands

Department of Cognitive Science and Artificial Intelligence,School of Humanities and Digital Sciences,Tilburg University,Tilburg,The Netherlands

2024

10.1111/deci.12653

Decision sciences: The journal for the American Institute for Decision Sciences

Decision sciences: The journal for the American Institute for Decision Sciences

ISSN：0011-7315
年,卷(期)：2024.55(6)
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