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A year of pandemic: levels, changes and validity of well-being data from Twitter. Evidence from ten countries

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  • Sarracino, Francesco
  • Greyling, Talita
  • O'Connor , Kelsey
  • Peroni, Chiara
  • Rossouw, Stephanie

Abstract

In this article we describe how well-being changed during 2020 in ten countries, namely Australia, Belgium, France, Germany, Great Britain, Italy, Luxembourg, New Zealand, South Africa, and Spain. Our measure of well-being is the Gross National Happiness (GNH), a country-level index built applying sentiment analysis to data from Twitter. Our aim is to describe how GNH changed during the pandemic within countries, to assess its validity as a measure of well-being, and to analyse its correlates. We take advantage of a unique data-set made of daily observations about GNH, generalized trust and trust in national institutions, fear concerning the economy, loneliness, infection rate, policy stringency and distancing. To assess the validity of data sourced from Twitter, we exploit various sources of survey data, such as Eurobarometer and consumer satisfaction, and big data, such as Google Trends. Results indicate that sentiment analysis of Tweets an provide reliable and timely information on well-being. This can be particularly useful to timely inform decision-making.

Suggested Citation

  • Sarracino, Francesco & Greyling, Talita & O'Connor , Kelsey & Peroni, Chiara & Rossouw, Stephanie, 2021. "A year of pandemic: levels, changes and validity of well-being data from Twitter. Evidence from ten countries," GLO Discussion Paper Series 831, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:831
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    References listed on IDEAS

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    6. repec:hal:pseose:halshs-01383778 is not listed on IDEAS
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    Cited by:

    1. Francesco Sarracino & Kelsey J. O’Connor, 2023. "Neo-humanism and COVID-19: Opportunities for a socially and environmentally sustainable world," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 18(1), pages 9-41, February.
    2. Sarracino, Francesco & Greyling, Talita & O'Connor, Kelsey J. & Peroni, Chiara & Rossouw, Stephanie, 2024. "Trust predicts compliance with COVID-19 containment policies: Evidence from ten countries using big data," Economics & Human Biology, Elsevier, vol. 54(C).
    3. Greyling, Talita & Rossouw, Stephanié, 2022. "Re-examining adaptation theory using Big Data: Reactions to external shocks," GLO Discussion Paper Series 1129, Global Labor Organization (GLO).
    4. Francesco Sarracino & Talita Greyling & Kelsey J. O'Connor & Chiara Peroni & Stephanie Rossouw, 2021. "Trust predicts compliance to Covid-19 containment policies: evidence from ten countries using big data," Department of Economics University of Siena 858, Department of Economics, University of Siena.

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    More about this item

    Keywords

    happiness; Covid-19; Big Data; Twitter; Sentiment Analysis; well-being; public policy; trust; fear; loneliness;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management

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