Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals

Abstract The United Nations’ (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are e...

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Autores principales:	Thomas Bryan Smith, Raffaele Vacca, Luca Mantegazza, Ilaria Capua
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e2b54f43551a4fb49ed1fa4f1ba903a3
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spelling	oai:doaj.org-article:e2b54f43551a4fb49ed1fa4f1ba903a32021-11-21T12:19:03ZNatural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals10.1038/s41598-021-01801-62045-2322https://doaj.org/article/e2b54f43551a4fb49ed1fa4f1ba903a32021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01801-6https://doaj.org/toc/2045-2322Abstract The United Nations’ (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are either theoretical investigations of sustainability concepts, or empirical analyses of development indicators and policy simulations. We present an alternative approach, which describes and quantifies the complex network of SDG interdependencies by applying computational methods to policy and scientific documents. Methods of Natural Language Processing are used to measure overlaps in international policy discourse around SDGs, as represented by the corpus of all existing UN progress reports about each goal (N = 85 reports). We then examine if SDG interdependencies emerging from UN discourse are reflected in patterns of integration and collaboration in SDG-related science, by analyzing data on all scientific articles addressing relevant SDGs in the past two decades (N = 779,901 articles). Results identify a strong discursive divide between environmental goals and all other SDGs, and unexpected interdependencies between SDGs in different areas. While UN discourse partially aligns with integration patterns in SDG-related science, important differences are also observed between priorities emerging in UN and global scientific discourse. We discuss implications and insights for scientific research and policy on sustainable development after COVID-19.Thomas Bryan SmithRaffaele VaccaLuca MantegazzaIlaria CapuaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Thomas Bryan Smith Raffaele Vacca Luca Mantegazza Ilaria Capua Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals
description	Abstract The United Nations’ (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are either theoretical investigations of sustainability concepts, or empirical analyses of development indicators and policy simulations. We present an alternative approach, which describes and quantifies the complex network of SDG interdependencies by applying computational methods to policy and scientific documents. Methods of Natural Language Processing are used to measure overlaps in international policy discourse around SDGs, as represented by the corpus of all existing UN progress reports about each goal (N = 85 reports). We then examine if SDG interdependencies emerging from UN discourse are reflected in patterns of integration and collaboration in SDG-related science, by analyzing data on all scientific articles addressing relevant SDGs in the past two decades (N = 779,901 articles). Results identify a strong discursive divide between environmental goals and all other SDGs, and unexpected interdependencies between SDGs in different areas. While UN discourse partially aligns with integration patterns in SDG-related science, important differences are also observed between priorities emerging in UN and global scientific discourse. We discuss implications and insights for scientific research and policy on sustainable development after COVID-19.
format	article
author	Thomas Bryan Smith Raffaele Vacca Luca Mantegazza Ilaria Capua
author_facet	Thomas Bryan Smith Raffaele Vacca Luca Mantegazza Ilaria Capua
author_sort	Thomas Bryan Smith
title	Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals
title_short	Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals
title_full	Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals
title_fullStr	Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals
title_full_unstemmed	Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals
title_sort	natural language processing and network analysis provide novel insights on policy and scientific discourse around sustainable development goals
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e2b54f43551a4fb49ed1fa4f1ba903a3
work_keys_str_mv	AT thomasbryansmith naturallanguageprocessingandnetworkanalysisprovidenovelinsightsonpolicyandscientificdiscoursearoundsustainabledevelopmentgoals AT raffaelevacca naturallanguageprocessingandnetworkanalysisprovidenovelinsightsonpolicyandscientificdiscoursearoundsustainabledevelopmentgoals AT lucamantegazza naturallanguageprocessingandnetworkanalysisprovidenovelinsightsonpolicyandscientificdiscoursearoundsustainabledevelopmentgoals AT ilariacapua naturallanguageprocessingandnetworkanalysisprovidenovelinsightsonpolicyandscientificdiscoursearoundsustainabledevelopmentgoals
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Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals

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