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

Author

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  • Andrea Sciandra

    (University of Modena and Reggio Emilia)

  • Alessio Surian

    (University of Padova)

  • Livio Finos

    (University of Padova)

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.

Suggested Citation

  • Andrea Sciandra & Alessio Surian & Livio Finos, 2021. "Supervised Machine Learning Methods to Disclose Action and Information in “U.N. 2030 Agenda” Social Media Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 689-699, August.
    • Handle: RePEc:spr:soinre:v:156:y:2021:i:2:d:10.1007_s11205-020-02523-4
    DOI: 10.1007/s11205-020-02523-4
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    References listed on IDEAS

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    1. Charlene Boyom & Stéphane Callens & Sofiane Cherfi, 2016. "Cultures of sustainability according to Ulrich Beck scheme: territorial strategies for electromobility," Journal of Innovation Economics, De Boeck Université, vol. 0(1), pages 135-158.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    3. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    4. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
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    Cited by:

    1. Yao Li & Michael Rockinger, 2024. "Unfolding the Transitions in Sustainability Reporting," Sustainability, MDPI, vol. 16(2), pages 1-31, January.
    2. Juan A. García-Esparza & Javier Pardo & Pablo Altaba & Mario Alberich, 2023. "Validity of Machine Learning in Assessing Large Texts Through Sustainability Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 166(2), pages 323-337, April.

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