Supervised Machine Learning Methods to Disclose Action and Information in “U.N. 2030 Agenda” Social Media Data

Andrea Sciandra (), Alessio Surian () and Livio Finos ()
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Andrea Sciandra: University of Modena and Reggio Emilia
Alessio Surian: University of Padova
Livio Finos: University of Padova

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2021, vol. 156, issue 2, No 18, 689-699

Abstract: Abstract In 2015, the United Nation General Assembly adopted the 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals aiming at ending all forms of poverty, fighting inequalities, and tackling climate change. We collected Twitter data about the 2030 Agenda from May 9th to November 9th, 2018. The aim of this work is to obtain a classification of each tweet in the corpus according to the “Information”—“Action” categories, in order to detect whether a tweet refers to an event or it has only an informative-disclosure purpose. It seems particularly interesting to understand how and to what extent people and organizations are playing a more active role in shaping the process of responding locally and internationally to climate change. Explicit intention to act or inform had been captured by hand coding of a randomly selected sample of tweets and then the classification had been extended to the whole corpus through a supervised machine learning method. Overall, our classification supervised model has produced satisfactory results.

Keywords: Supervised machine learning; Textual data analysis; Sustainable development goals; Social media (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11205-020-02523-4

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