Art is long, life is short: An SDG Classification System for DESA Publications

Marcelo T. LaFleur

Working Papers from United Nations, Department of Economics and Social Affairs

Abstract: Between the many resolutions, speeches, reports and other documents that are produced each year, the United Nations is awash in text. It is an ongoing challenge to create a coherent and useful picture of this corpus. In particular, there is an interest in measuring how the work of the United Nations system aligns with the Sustainable Development Goals (SDGs). There is a need for a scalable, objective, and consistent way to measure how similar any given publication is to each of the 17 SDGs. This paper explains a proof-of-concept process for building such a system using machine learning algorithms. By creating a model of the 17 SDGs it is possible to measure how similar the contents of individual publications are to each of the goals — their SDG Score. This paper also shows how this system can be used in practice by computing the SDG Scores for a limited selection of DESA publications and providing some analytics.

Keywords: SDG; publications; classification; topic models; machine learning, LDA (search for similar items in EconPapers)
JEL-codes: C88 O0 O20 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2019-05
New Economics Papers: this item is included in nep-cmp
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:une:wpaper:159

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