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Mental health effects of COVID-19 lockdowns: a Twitter-based analysis

Author

Listed:
  • Sara Colella

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Frédéric Dufourt

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Vincent A Hildebrand

    (York University [Toronto], Dalla Lana School of Public Health, University of Toronto)

  • Rémi Vivès

    (York University [Toronto])

Abstract

We use a distinctive methodology that leverages a fixed population of Twitter users located in France to gauge the mental health effects of repeated lockdown orders. To do so, we derive from our population a mental health indicator that measures the frequency of words expressing anger, anxiety and sadness. Our indicator did not reveal a statistically significant mental health response during the first lockdown, while the second lockdown triggered a sharp and persistent deterioration in all three emotions. Our estimates also show a more severe deterioration in mental health among women and younger users during the second lockdown. These results suggest that successive stay-at-home orders significantly worsen mental health across a large segment of the population. We also show that individuals who are closer to their social network were partially protected by this network during the first lockdown, but were no longer protected during the second, demonstrating the gravity of successive lockdowns for mental health.

Suggested Citation

  • Sara Colella & Frédéric Dufourt & Vincent A Hildebrand & Rémi Vivès, 2022. "Mental health effects of COVID-19 lockdowns: a Twitter-based analysis," Working Papers hal-03740701, HAL.
    • Handle: RePEc:hal:wpaper:hal-03740701
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-03740701v2
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    More about this item

    Keywords

    COVID-19; lockdown; mental health; Twitter data; well-being;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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