Text mining and machine learning reveal global determinants of food insecurity
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 s...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/177245 |
| _version_ | 1855513750046507008 |
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| author | Carneiro, Bia Resce, Giuliano Caravaggio, Nicola Santangelo, Agapito Emanuele Ruscica, Giosue Tucci, Giulia Pacillo, Grazia Eilert, Gary Läderach, Peter Coffey, Kevin |
| author_browse | Caravaggio, Nicola Carneiro, Bia Coffey, Kevin Eilert, Gary Läderach, Peter Pacillo, Grazia Resce, Giuliano Ruscica, Giosue Santangelo, Agapito Emanuele Tucci, Giulia |
| author_facet | Carneiro, Bia Resce, Giuliano Caravaggio, Nicola Santangelo, Agapito Emanuele Ruscica, Giosue Tucci, Giulia Pacillo, Grazia Eilert, Gary Läderach, Peter Coffey, Kevin |
| author_sort | Carneiro, Bia |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace177245 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1772452025-11-11T17:39:23Z Text mining and machine learning reveal global determinants of food insecurity Carneiro, Bia Resce, Giuliano Caravaggio, Nicola Santangelo, Agapito Emanuele Ruscica, Giosue Tucci, Giulia Pacillo, Grazia Eilert, Gary Läderach, Peter Coffey, Kevin machine learning food security modelling text mining 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. 2025-10 2025-10-21T13:06:40Z 2025-10-21T13:06:40Z Journal Article https://hdl.handle.net/10568/177245 en Open Access application/pdf Springer Carneiro, B.; Resce, G.; Caravaggio, N.; Santangelo, A.E.; Ruscica, G.; Tucci, G.; Pacillo, G.; Eilert, G.; Läderach, P.; Coffey, K. (2025) Text mining and machine learning reveal global determinants of food insecurity. Scientific Reports 15: 36709 . ISSN: 2045-2322 |
| spellingShingle | machine learning food security modelling text mining Carneiro, Bia Resce, Giuliano Caravaggio, Nicola Santangelo, Agapito Emanuele Ruscica, Giosue Tucci, Giulia Pacillo, Grazia Eilert, Gary Läderach, Peter Coffey, Kevin Text mining and machine learning reveal global determinants of food insecurity |
| title | Text mining and machine learning reveal global determinants of food insecurity |
| title_full | Text mining and machine learning reveal global determinants of food insecurity |
| title_fullStr | Text mining and machine learning reveal global determinants of food insecurity |
| title_full_unstemmed | Text mining and machine learning reveal global determinants of food insecurity |
| title_short | Text mining and machine learning reveal global determinants of food insecurity |
| title_sort | text mining and machine learning reveal global determinants of food insecurity |
| topic | machine learning food security modelling text mining |
| url | https://hdl.handle.net/10568/177245 |
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