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Big Data and Social Indicators: Actual Trends and New Perspectives

Author

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  • Enrico di Bella

    (University of Genoa)

  • Lucia Leporatti

    (University of Genoa)

  • Filomena Maggino

    (University of Florence)

Abstract

Big Data are a top subject in international research articles and a vast debate is taking place on their actual capability of being used to complement or even substitute official statistics surveys and social indicators in particular. In this paper we analyse the metadata of the Scopus database of academic articles on Big Data and we show that most of the existing and intensively growing literature is focused on software and computational issues whilst articles that are specifically focused on statistical issues and on the procedures to build social indicators from Big Data are a much smaller share of this vast production. Nevertheless the works that focus on these topics show promising results because in developed countries Big Data seem to be a good information base to create reliable proxies of social indicators, whereas in developing countries their use (for instance using satellite images) may be a viable alternative to traditional surveys. However, Big Data based social indicators deeply suffer of a number of open issues that affect their actual use: they do not correspond to any sampling scheme and they are often representative of particular segments of the population; they generally are private process-produced data whose access by national statistical offices is rarely possible although the intrinsic value of the information contained in Big Data has a social importance that should be shared with the whole community; Big Data lack the socio-economic background on which social indicators have been founded and their help to policy makers in their decision process is a fully open point. Therefore Big Data may be a big opportunity for the definition of traditional or new social indicators but their statistical reliability should be further investigated and their availability and use should be internationally coordinated.

Suggested Citation

  • Enrico di Bella & Lucia Leporatti & Filomena Maggino, 2018. "Big Data and Social Indicators: Actual Trends and New Perspectives," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(3), pages 869-878, February.
  • Handle: RePEc:spr:soinre:v:135:y:2018:i:3:d:10.1007_s11205-016-1495-y
    DOI: 10.1007/s11205-016-1495-y
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    References listed on IDEAS

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    2. Waggoner Philip D. & Kennedy Ryan & Shiran Myriam & Le Hayden, 2019. "Big Data and Trust in Public Policy Automation," Statistics, Politics and Policy, De Gruyter, vol. 10(2), pages 115-136, December.
    3. Venera Tomaselli & Giovanni Giuffrida & Simona Gozzo & Francesco Mazzeo Rinaldi, 2020. "Building Decision-making Indicators Through Network Analysis of Big Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(1), pages 33-49, August.
    4. El-Haddadeh, Ramzi & Osmani, Mohamad & Hindi, Nitham & Fadlalla, Adam, 2021. "Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics," Journal of Business Research, Elsevier, vol. 131(C), pages 402-410.
    5. Ana Maria Aguilera & Francesca Fortuna & Manuel Escabias & Tonio Di Battista, 2021. "Assessing Social Interest in Burnout Using Google Trends Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 587-599, August.
    6. Garbero, Alessandra & Carneiro, Bia & Resce, Giuliano, 2021. "Harnessing the power of machine learning analytics to understand food systems dynamics across development projects," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    7. Mohammad Reza Farzanegan & Mehdi Feizi & Saeed Malek Sadati, 2020. "Google It Up! A Google Trends-based analysis of COVID-19 outbreak in Iran," MAGKS Papers on Economics 202017, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Vydra Simon & Kantorowicz Jaroslaw, 2021. "Tracing Policy-relevant Information in Social Media: The Case of Twitter before and during the COVID-19 Crisis," Statistics, Politics and Policy, De Gruyter, vol. 12(1), pages 87-127, June.
    9. Rodolfo Metulini & Maurizio Carpita, 2021. "A Spatio-Temporal Indicator for City Users Based on Mobile Phone Signals and Administrative Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 761-781, August.
    10. Yukun Zhao & Feng Yu & Bo Jing & Xiaomeng Hu & Ang Luo & Kaiping Peng, 2019. "An Analysis of Well-Being Determinants at the City Level in China Using Big Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(3), pages 973-994, June.
    11. Fatehkia, Masoomali & Kashyap, Ridhi & Weber, Ingmar, 2018. "Using Facebook ad data to track the global digital gender gap," World Development, Elsevier, vol. 107(C), pages 189-209.

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