Food insecurity trends in the Famine Early Warning Systems Network

[EN] Over last 30 years, periodic country analyses elaborated by FEWS NET (Famine Early Warning Systems Network of the United States Agency for International Development) enabled creation of a unique source of knowledge comprising consistent reporting in over two dozen countries. This paper proposes...

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Detalles Bibliográficos
Autores: Carneiro, Bia, Perfetto, Chiara, Resce, Giuliano, Ruscica, Giosuè, Tucci, Giulia
Tipo de recurso: capítulo de libro
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/201788
Acceso en línea:https://riunet.upv.es/handle/10251/201788
Access Level:acceso abierto
Palabra clave:Food insecurity
Early Warning Systems
Text Mining
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spelling Food insecurity trends in the Famine Early Warning Systems NetworkCarneiro, BiaPerfetto, ChiaraResce, GiulianoRuscica, GiosuèTucci, GiuliaFood insecurityEarly Warning SystemsText Mining[EN] Over last 30 years, periodic country analyses elaborated by FEWS NET (Famine Early Warning Systems Network of the United States Agency for International Development) enabled creation of a unique source of knowledge comprising consistent reporting in over two dozen countries. This paper proposes to systematically assess documentation from historical perspective to provide comprehensive overview of food insecurity in FEWS NET covered countries. We propose an integrated machine learning approach to systematically analyse available documentation and generate knowledge. In particular text mining algorithms have been implemented to analyse reports: automated retrieval of high-quality information from text, by finding patterns and trends through machine learning, statistics and linguistics. This enables analysis of large amounts of unstructured text to derive insights. Results show that there is a wide heterogeneity in what is relevant, and in what reports focus on at the territorial level. Many country-level topics are persistent over time with some interesting exception, as Guatemala, Malawi, Niger, and Somalia with more instability. Overall, the evidence show that advances in machine learning and Big Data research offer great potential for international development agencies to leverage the vast information generated from reports to gain new insights, providing analytics that can improve decision-making.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-09-22book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/201788reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2017882026-06-13T07:49:27Z
dc.title.none.fl_str_mv Food insecurity trends in the Famine Early Warning Systems Network
title Food insecurity trends in the Famine Early Warning Systems Network
spellingShingle Food insecurity trends in the Famine Early Warning Systems Network
Carneiro, Bia
Food insecurity
Early Warning Systems
Text Mining
title_short Food insecurity trends in the Famine Early Warning Systems Network
title_full Food insecurity trends in the Famine Early Warning Systems Network
title_fullStr Food insecurity trends in the Famine Early Warning Systems Network
title_full_unstemmed Food insecurity trends in the Famine Early Warning Systems Network
title_sort Food insecurity trends in the Famine Early Warning Systems Network
dc.creator.none.fl_str_mv Carneiro, Bia
Perfetto, Chiara
Resce, Giuliano
Ruscica, Giosuè
Tucci, Giulia
author Carneiro, Bia
author_facet Carneiro, Bia
Perfetto, Chiara
Resce, Giuliano
Ruscica, Giosuè
Tucci, Giulia
author_role author
author2 Perfetto, Chiara
Resce, Giuliano
Ruscica, Giosuè
Tucci, Giulia
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Food insecurity
Early Warning Systems
Text Mining
topic Food insecurity
Early Warning Systems
Text Mining
description [EN] Over last 30 years, periodic country analyses elaborated by FEWS NET (Famine Early Warning Systems Network of the United States Agency for International Development) enabled creation of a unique source of knowledge comprising consistent reporting in over two dozen countries. This paper proposes to systematically assess documentation from historical perspective to provide comprehensive overview of food insecurity in FEWS NET covered countries. We propose an integrated machine learning approach to systematically analyse available documentation and generate knowledge. In particular text mining algorithms have been implemented to analyse reports: automated retrieval of high-quality information from text, by finding patterns and trends through machine learning, statistics and linguistics. This enables analysis of large amounts of unstructured text to derive insights. Results show that there is a wide heterogeneity in what is relevant, and in what reports focus on at the territorial level. Many country-level topics are persistent over time with some interesting exception, as Guatemala, Malawi, Niger, and Somalia with more instability. Overall, the evidence show that advances in machine learning and Big Data research offer great potential for international development agencies to leverage the vast information generated from reports to gain new insights, providing analytics that can improve decision-making.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-09-22
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/201788
url https://riunet.upv.es/handle/10251/201788
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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