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What matters most to people around the world? Retrieving Better Life Index priorities on Twitter

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  • Resce, Giuliano
  • Maynard, Diana

Abstract

Better Life Index (BLI), the measure of well-being proposed by the OECD, contains many metrics, which enable it to include a detailed overview of the social, economic, and environmental performances of different countries. However, this also increases the difficulty in evaluating the big picture. In order to overcome this, many composite BLI procedures have been proposed, but none of them takes into account societal priorities in the aggregation. One of the reasons for this is that at the moment there is no representative survey about the relative priorities of the BLI topics for each country. Using these priorities could help to design Composite Indices that better reflect the needs of the people. The largest collection of information about society is found in social media such as Twitter. This paper proposes a composite BLI based on the weighted average of the national performances in each dimension of the BLI, using the relative importance that the topics have on Twitter as weights. The idea is that the aggregate of millions of tweets may provide a representation of the priorities (the relative appreciations) among the eleven topics of the BLI, both at a general level and at a country-specific level. By combining topic performances and related Twitter trends, we produce new evidences about the relations between people's priorities and policy makers' activity in the BLI framework.

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  • Resce, Giuliano & Maynard, Diana, 2018. "What matters most to people around the world? Retrieving Better Life Index priorities on Twitter," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 61-75.
  • Handle: RePEc:eee:tefoso:v:137:y:2018:i:c:p:61-75
    DOI: 10.1016/j.techfore.2018.06.044
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    2. Greco, Salvatore & Ishizaka, Alessio & Resce, Giuliano & Torrisi, Gianpiero, 2020. "Measuring well-being by a multidimensional spatial model in OECD Better Life Index framework," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    3. Giuliano Resce & Fritz Schiltz, 2021. "Sustainable Development in Europe: A Multicriteria Decision Analysis," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(2), pages 509-529, June.
    4. Nevado-Peña, Domingo & López-Ruiz, Víctor-Raúl & Alfaro-Navarro, José-Luis, 2019. "Improving quality of life perception with ICT use and technological capacity in Europe," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    5. Mishal J. Al-Thani & Muammer Koç, 2024. "In Search of Sustainable Economy Indicators: A Comparative Analysis between the Sustainable Development Goals Index and the Green Growth Index," Sustainability, MDPI, vol. 16(4), pages 1-33, February.
    6. Alessandra Garbero & Giuliano Resce & Bia Carneiro, 2021. "Spatial dynamics across food systems transformation in IFAD investments: a machine learning approach," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(5), pages 1125-1143, October.
    7. Loretta Mastroeni & Maurizio Naldi & Pierluigi Vellucci, 2023. "Who pushes the discussion on wind energy? An analysis of self-reposting behaviour on Twitter," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1763-1789, April.
    8. Bhatti, Zeeshan Ahmed & Arain, Ghulam Ali & Akram, Muhammad Shakaib & Fang, Yu-Hui & Yasin, Hina Mahboob, 2020. "Constructive voice behavior for social change on social networking sites: A reflection of moral identity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    9. Ridhi Kashyap & Masoomali Fatehkia & Reham Al Tamime & Ingmar Weber, 2020. "Monitoring global digital gender inequality using the online populations of Facebook and Google," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(27), pages 779-816.
    10. 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).
    11. Tae‐Hyoung T. Gim, 2021. "Partial least squares regression and importance–satisfaction analyses of the strategic drivers of happiness: A quality of life survey in Seoul, Korea," Growth and Change, Wiley Blackwell, vol. 52(1), pages 567-599, March.

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    More about this item

    Keywords

    Big Data; Better Life Index; Composite indicators; Twitter;
    All these keywords.

    JEL classification:

    • A13 - General Economics and Teaching - - General Economics - - - Relation of Economics to Social Values
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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