The Impact of COVID-19 on Subjective Well-Being: Evidence from Twitter Data

Carpi, Tiziana; Hino, Airo; Iacus, Stefano Maria; Porro, Giuseppe

doi:10.6339/22-JDS1066

Journal of Data Science

The Impact of COVID-19 on Subjective Well-Being: Evidence from Twitter Data

Volume 21, Issue 4 (2023), pp. 761–780

Tiziana Carpi Airo Hino Stefano Maria Iacus All authors (4)

https://doi.org/10.6339/22-JDS1066

Pub. online: 29 September 2022 Type: Data Science In Action

Open Access

Received
14 January 2022

Accepted
16 September 2022

Published
29 September 2022

Abstract

This study analyzes the impact of the COVID-19 pandemic on subjective well-being as measured through Twitter for the countries of Japan and Italy. In the first nine months of 2020, the Twitter indicators dropped by 11.7% for Italy and 8.3% for Japan compared to the last two months of 2019, and even more compared to their historical means. To understand what affected the Twitter mood so strongly, the study considers a pool of potential factors including: climate and air quality data, number of COVID-19 cases and deaths, Facebook COVID-19 and flu-like symptoms global survey data, coronavirus-related Google search data, policy intervention measures, human mobility data, macro economic variables, as well as health and stress proxy variables. This study proposes a framework to analyse and assess the relative impact of these external factors on the dynamic of Twitter mood and further implements a structural model to describe the underlying concept of subjective well-being. It turns out that prolonged mobility restrictions, flu and Covid-like symptoms, economic uncertainty and low levels of quality in social interactions have a negative impact on well-being.

Supplementary material

Supplementary Material

The supplementary material consists of the following sections: Construction of the Twitter indicators; Stochastic analysis; Dynamic Elastic Net and Dynamic variable selection for the SWB-I/J indicators; Structural equation models. As well as information on authors’ contribution and data availability.

References

Asaoka H, Koido Y, Kawashima Y, Ikeda M, Miyamoto Y, Nishi D (2020). Post-traumatic stress symptoms among medical rescue workers exposed to COVID-19 in Japan. Psychiatry and Clinical Neurosciences, 74(9): 503–505.

Baldwin R, Weder di Mauro B (2020). Economics in the Time of COVID-19. CEPR Press.

Bollen K (1989). Structural Equations with Latent Variables. Wiley, New York.

Breiman L (2001). Random forests. Machine Learning, 45(1): 5–32.

Capuano R, Altieri M, Bisecco A, d’Ambrosio A, Docimo R, Buonanno D, et al. (2020). Psychological consequences of COVID-19 pandemic in Italian MS patients: Signs of resilience? Journal of Neurology, 268(3): 743–750.

Carammia M, Iacus SM, Wilkin T (2022). Forecasting asylum-related migration flows with machine learning and data at scale. Scientific Reports, 12(1): 1457.

Carpi T, Hino A, Iacus SM, Porro G (2022). A Japanese subjective well-being indicator based on Twitter data. Social Science Japan Journal, 25(2): 273–296.

Ceron A, Curini L, Iacus SM (2016a). First- and second-level agenda setting in the twittersphere: An application to the Italian political debate. Journal of Information Technology & Politics, 13(2): 159–174.

Ceron A, Curini L, Iacus SM (2016b). iSA: A fast, scalable and accurate algorithm for sentiment analysis of social media content. Information Sciences, 367(C): 105–124.

Chan KC, Karolyi GA, Longstaff FA, Sanders AB (1992). An empirical comparison of alternative models of the short-term interest rate. The Journal of Finance, 47(3): 1209–1227.

Choi H, Varian H (2012). Predicting the present with Google trends. Economic Record, 88(s1): 2–9.

Chudik A, Mohaddes K, Pesaran MH, Raissi M, Rebucci A (2020). A counterfactual economic analysis of COVID-19 using a threshold augmented multi-country model. Working Paper 27855, National Bureau of Economic Research. http://www.nber.org/papers/w27855.

Coppola I, Rania N, Parisi R, Lagomarsino F (2021). Spiritual well-being and mental health during the COVID-19 pandemic in Italy. Frontiers in Psychiatry, 12(626944): 1–15.

Curini L, Iacus S, Canova L (2015). Measuring idiosyncratic happiness through the analysis of Twitter: An application to the Italian case. Social Indicators Research, 121(2): 525–542.

Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM (2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS ONE, 6(12): 1–26.

Döring N (2020). How is the COVID-19 pandemic affecting our sexualities? An overview of the current media narratives and research hypotheses. Archives of Sexual Behavior, 49(8): 2765–2778.

D’Orlando F (2011). The demand for pornography. Journal of Happiness Studies, 12(1): 51–75.

Fan J, Li Y, Stewart K, Kommareddy AR, Bradford A, Chiu S, et al. (2020). COVID-19 world symptom survey data api, Technical report, University of Maryland. https://covidmap.umd.edu/api.html.

Gallè F, Sabella EA, Roma P, Ferracuti S, Da Molin G, Diella G, et al. (2021). Knowledge and lifestyle behaviors related to COVID-19 pandemic in people over 65 years old from southern Italy. International Journal of Environmental Research and Public Health, 18(20): 1–11.

Greyling T, Rossouw S, Adhikari T (2020). Happiness-lost: Did governments make the right decisions to combat COVID-19? Repec preprint, Global Labor Organization. https://ideas.repec.org/p/zbw/glodps/556.html.

Gualano MR, Lo Moro G, Voglino G, Bert F, Siliquini R (2020). Effects of COVID-19 lockdown on mental health and sleep disturbances in Italy. International Journal of Environmental Research and Public Health, 17(4779): 1–13.

Guntuku SC, Sherman G, Stokes DC, Agarwal AK, Seltzer E, Merchant RM, et al. (2020). Tracking mental health and symptom mentions on Twitter during COVID-19. Journal of General Internal Medicine, 35(9): 2798–2800.

Haruna M, Nishi D (2020). Perinatal mental health and COVID-19 in Japan. Psychiatry and Clinical Neurosciences, 74(9): 502–503.

Hino A, Fahey RA (2019). Representing the Twittersphere: Archiving a representative sample of Twitter data under resource constraints. International Journal of Information Management, 48: 175–184.

Iacus SM (2008). Simulation and Inference for Stochastic Differential Equations. With R Examples. Springer, New York.

Iacus SM, Yoshida N (2018). Simulation and Inference for Stochastic Processes with YUIMA: A Comprehensive R Framework for SDEs and Other Stochastic Processes. Springer, New York.

Iacus SM, Porro G, Salini S, Siletti E (2019). Social networks data and subjective well-being. An innovative measurement for Italian provinces. Scienze Regionali, Italian Journal of Regional Science, 2019(Speciale): 667–678.

Iacus SM, Porro G, Salini S, Siletti E (2020a). Controlling for selection bias in social media indicators through official statistics: A proposal. Journal of Official Statistics, 36(2): 315–338.

Iacus SM, Porro G, Salini S, Siletti E (2020b). An Italian composite subjective well-being index: The voice of Twitter users from 2012 to 2017. Social Indicators Research, 161: 1–31.

Koda M, Harada N, Eguchi A, Nomura S, Ishida Y (2022). Reasons for suicide during the COVID-19 pandemic in Japan. JAMA Network Open, 5: e2145870.

Kotera Y, Ozaki A, Miyatake H, Tsunetoshi C, Nishikawa Y, Kosaka M, et al. (2022). Qualitative investigation into the mental health of healthcare workers in Japan during the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 19(1): 568.

Marazziti D, Pozza A, Di Giuseppe M, Conversano C (2020). The psychosocial impact of COVID-19 pandemic in Italy: A lesson for mental health prevention in the first severely hit European country. Psychological Trauma: Theory, Research, Practice, and Policy, 12: 531–533.

Massicotte P, Eddelbuettel D (2020). gtrendsR: Perform and display Google trends queries. R package, R Foundation for Statistical Computing. https://CRAN.R-project.org/package=gtrendsR.

Matsushima M, Horiguchi H (2022). The COVID-19 pandemic and mental well-being of pregnant women in Japan: Need for economic and social policy interventions. Disaster Medicine and Public Health Preparedness, 16: 449–454.

Maugeri G, Castrogiovanni P, Battaglia G, Pippi R, D’Agata V, Palma A, et al. (2020). The impact of physical activity on psychological health during COVID-19 pandemic in Italy. Heliyon, 6: 1–8.

Mestre-Bach G, Blycker GR, Potenza MN (2020). Pornography use in the setting of the COVID-19 pandemic. Journal of Behavioral Addictions, 9(2): 181–183.

New Economic Foundation (2009). National accounts of well-being: bringing real wealth onto the balance sheet. NEF report, New Economics Foundation. https://neweconomics.org/uploads/files/2027fb05fed1554aea_uim6vd4c5.pdf.

New Economic Foundation (2012). Measuring well-being. A guide for practitioners. NEF report, New Economics Foundation. https://neweconomics.org/uploads/files/measuring-wellbeing.pdf.

OECD (2021). COVID-19 and Well-Being: Life in the Pandemic. OECD Publishing.

Orgilés M, Morales A, Delvecchio E, Mazzeschi C, Espada JP (2020). Immediate psychological effects of the COVID-19 quarantine in youth from Italy and Spain. Frontiers in Psychology, 11: 1–10.

Qian K, Yahara T (2020). Mentality and behavior in COVID-19 emergency status in Japan: Influence of personality, morality and ideology. PLoS ONE, 15: 1–16.

Ravaldi C, Wilson A, Ricca V, Homer C, Vannacci A (2021). Pregnant women voice their concerns and birth expectations during the COVID-19 pandemic in Italy. Women and Birth, 34: 335–343.

Rossi R, Socci V, Talevi D, Mensi S, Niolu C, Pacitti F, et al. (2020). COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy. Frontiers in Psychiatry, 11: 1–6.

Rossi R, Socci V, Jannini TB, Pacitti F, Siracusano A, Rossi A, et al. (2021). Mental health outcomes among Italian health care workers during the COVID-19 pandemic. JAMA Network Open, 4: 1–11.

Rossouw S, Greyling T (2020). Big data and happiness. In: Handbook of Labor, Human Resources and Population Economics (K Zimmermann, ed.), 1–35. Springer.

Ryan JA, Ulrich JM (2020). quantmod: Quantitative financial modelling framework. R package, R Foundation for Statistical Computing. https://CRAN.R-project.org/package=quantmod.

Sani G, Janiri D, Di Nicola M, Janiri L, Ferretti S, Chieffo D (2020). Mental health during and after the COVID-19 emergency in Italy. Psychiatry and Clinical Neurosciences, 74: 372.

Shigemura J, Kurosawa M (2020). Mental health impact of the COVID-19 pandemic in Japan. Psychological Trauma: Theory, Research, Practice, and Policy, 12: 478–479.

Shigemura RJ, Ursano J, Morganstein JC, Kurosawa M, Benedek DM (2020). Public responses to the novel 2019 coronavirus (2019-nCoV) in Japan: Mental health consequences and target populations. Psychiatry and Clinical Neurosciences, 74: 281–282.

Torricelli L, Poletti M, Raballo A (2021). Managing COVID-19 related psychological distress in health workers: field experience in northern Italy. Psychiatry and Clinical Neurosciences, 75(1): 23–24.

Ueda M, Stickley A, Sueki H, Matsubayashi T (2020). Mental health status of the general population in Japan during the COVID-19 pandemic. Psychiatry and Clinical Neurosciences, 74: 505–506.

Yamamoto T, Uchiumi C, Suzuki N, Yoshimoto J, Murillo-Rodriguez E (2020). The psychological impact of ‘mild lockdown’ in Japan during the COVID-19 pandemic: A nationwide survey under a declared state of emergency. medRxiv preprint, 1(1): 1–24. https://www.medrxiv.org/content/medrxiv/early/2020/07/30/2020.07.17.20156125.full.pdf.

Yeyati EL, Filippini F (2021). The economic effects of COVID-19 containment measures. Brookings global working paper n. 158, Global Economy and Development Program at Brookings. https://www.brookings.edu/wp-content/uploads/2021/06/Social-and-economic-impact-COVID.pdf.

Zou H, Hastie T (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67: 301–320.

2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

COVID-19 subjective well-being Twitter data

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