Evidence map›Paper›PMID 41120495›Full record

ArticleScientific reports2025

Text mining and machine learning reveal global determinants of food insecurity.

Bia Carneiro, Giuliano Resce, Nicola Caravaggio, Agapito Emanuele Santangelo, Giosuè Ruscica, Giulia Tucci, Grazia Pacillo, Gary Eilerts, Peter Läderach, Kevin Coffey

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Bia CarneiroBioversity International, Rome, Italy. bia@4ctv.com.
Giuliano ResceDepartment of Economics, University of Molise, Campobasso, Italy.
Nicola CaravaggioDepartment of Economics, University of Molise, Campobasso, Italy.
Agapito Emanuele SantangeloDepartment of Economics, University of Molise, Campobasso, Italy.
Giosuè RuscicaBioversity International, Rome, Italy.
Giulia TucciInternational Center for Tropical Agriculture (CIAT), Cali, Colombia.
Grazia PacilloInternational Center for Tropical Agriculture (CIAT), Cairo, Egypt.
Gary EilertsFamine Early Warning Systems Network, Washington, USA.
Peter LäderachInternational Center for Tropical Agriculture (CIAT), Pretoria, South Africa.
Kevin CoffeyUnited States Agency for International Development, Bureau for Humanitarian Assistance, Washington, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study applies Natural Language Processing (NLP) and Machine Learning (ML) to investigate global historical trends in food security. Using USAID’s Famine Early Warning Systems Network’s (FEWS NET) comprehensive reports spanning over two dozen countries, it explores prevalent dimensions such as shocks, outcomes, and coping capacities, offering insights into long-term food security conditions. Results highlight the prevalence of market and climate impacts across the countries and period considered. Based on results from the topic classification, ML models were applied to determine the most important factors that predict food insecurity. The analysis confirmed market shocks as the main predictors of food insecurity globally. The approach demonstrates the potential for extracting valuable insights from narrative sources that can support decision-making and strategic planning. This integrated approach not only enhances understanding of food security but also presents a versatile tool applicable beyond the context of humanitarian aid.

Indexed as

Data MiningFood InsecurityFood SupplyMachine LearningHumansNatural Language ProcessingPredictive Learning Models

Identifiers

PMID41120495
PMCPMC12540806

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.