Art Is Long, Life Is Short: An SDG Classification System for DESA Publications

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

JEL Classification: O, O20, C88