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

Autor: Sarracino F; Research Division, Institut national de la statistique et des études économiques du Grand-Duché du Luxembourg, Luxembourg, Luxembourg., Greyling T; School of Economics, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa.; School of Social Science & Public Policy, Faculty of Culture & Society, Auckland University of Technology, Auckland, New Zealand., O'Connor K; Research Division, Institut national de la statistique et des études économiques du Grand-Duché du Luxembourg, Luxembourg, Luxembourg.; School of Economics, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa., Peroni C; Research Division, Institut national de la statistique et des études économiques du Grand-Duché du Luxembourg, Luxembourg, Luxembourg., Rossouw S; School of Economics, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa.; School of Social Science & Public Policy, Faculty of Culture & Society, Auckland University of Technology, Auckland, New Zealand.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Feb 10; Vol. 18 (2), pp. e0275028. Date of Electronic Publication: 2023 Feb 10 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0275028
Abstrakt: We use daily happiness scores (Gross National Happiness (GNH)) to illustrate how happiness changed throughout 2020 in ten countries across Europe and the Southern hemisphere. More frequently and regularly available than survey data, the GNH reveals how happiness sharply declined at the onset of the pandemic and lockdown, quickly recovered, and then trended downward throughout much of the year in Europe. GNH is derived by applying sentiment and emotion analysis-based on Natural Language Processing using machine learning algorithms-to Twitter posts (tweets). Using a similar approach, we generate another 11 variables: eight emotions and three new context-specific variables, in particular: trust in national institutions, sadness in relation to loneliness, and fear concerning the economy. Given the novelty of the dataset, we use multiple methods to assess validity. We also assess the correlates of GNH. The results indicate that GNH is negatively correlated with new COVID-19 cases, containment policies, and disgust and positively correlated with staying at home, surprise, and generalised trust. Altogether the analyses indicate tools based on Big Data, such as the GNH, offer relevant data that often fill information gaps and can valuably supplement traditional tools. In this case, the GNH results suggest that both the severity of the pandemic and containment policies negatively correlated with happiness.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Sarracino et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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