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This study analyzes the impact of the COVID-19 pandemic on the subjectivewell-being as measured through Twitter data indicators for Japan and Italy. Itturns out that, overall, the subjective well-being dropped by 11.7% for Italyand 8.3% for Japan in the first nine months of 2020 compared to the last twomonths of 2019 and even more compared to the historical mean of the indexes.Through a data science approach we try to identify the possible causes of thisdrop down by considering several explanatory variables including, climate andair quality data, number of COVID-19 cases and deaths, Facebook Covid and flusymptoms global survey, Google Trends data and coronavirus-related searches,Google mobility data, policy intervention measures, economic variables andtheir Google Trends proxies, as well as health and stress proxy variables basedon big data. We show that a simple static regression model is not able tocapture the complexity of well-being and therefore we propose a dynamic elasticnet approach to show how different group of factors may impact the well-beingin different periods, even over a short time length, and showing furthercountry-specific aspects. Finally, a structural equation modeling analysistries to address the causal relationships among the COVID-19 factors andsubjective well-being showing that, overall, prolonged mobilityrestrictions,flu and Covid-like symptoms, economic uncertainty, socialdistancing and news about the pandemic have negative effects on the subjectivewell-being.

Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study