Unsupervised news analysis for enhanced high-frequency food insecurity assessment
van Wanrooij,Cascha Cruijssen,Frans Olier,J.S.
van Wanrooij,Cascha
Cruijssen,Frans
Olier,J.S.
Abstract
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.
Description
Publisher Copyright: © 2024 The Author(s). Decision Sciences published by Wiley Periodicals LLC on behalf of Decision Sciences Institute.
Date
2024-12
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Somalia, food insecurity, news analysis, time series forecasting, unsupervised topic modeling, SDG 2 - Zero Hunger
Citation
van Wanrooij, C, Cruijssen, F & Olier, J S 2024, 'Unsupervised news analysis for enhanced high-frequency food insecurity assessment', Decision Sciences, vol. 55, no. 6, pp. 605-619. https://doi.org/10.1111/deci.12653
License
info:eu-repo/semantics/openAccess