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Volume 18
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Article

Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach

by
Federica Cugnata
1,
Silvia Salini
2,* and
Elena Siletti
3
1
University Centre of Statistics for Biomedical Sciences (CUSSB), Vita-Salute San Raffaele University, 20132 Milano, Italy
2
Department of Economics, Management and Quantitative Methods, Università degli Studi di Milano, 20122 Milano, Italy
3
Department of Economic and Political Sciences, Università della Valle d’Aosta, 11020 Saint-Christophe, Italy
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(15), 8110; https://doi.org/10.3390/ijerph18158110
Submission received: 28 June 2021 / Revised: 24 July 2021 / Accepted: 28 July 2021 / Published: 30 July 2021
(This article belongs to the Special Issue Advances in Measuring Health and Wellbeing)
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Abstract

In this paper, we focus on a Bayesian network s approach to combine traditional survey and social network data and official statistics to evaluate well-being. Bayesian networks permit the use of data with different geographical levels (provincial and regional) and time frequencies (daily, quarterly, and annual). The aim of this study was twofold: to describe the relationship between survey and social network data and to investigate the link between social network data and official statistics. Particularly, we focused on whether the big data anticipate the information provided by the official statistics. The applications, referring to Italy from 2012 to 2017, were performed using ISTAT’s survey data, some variables related to the considered time period or geographical levels, a composite index of well-being obtained by Twitter data, and official statistics that summarize the labor market.
Keywords: Bayesian networks; big data; well-being; life satisfaction; sentiment analysis (list three to ten) Bayesian networks; big data; well-being; life satisfaction; sentiment analysis (list three to ten)

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MDPI and ACS Style

Cugnata, F.; Salini, S.; Siletti, E. Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach. Int. J. Environ. Res. Public Health 2021, 18, 8110. https://doi.org/10.3390/ijerph18158110

AMA Style

Cugnata F, Salini S, Siletti E. Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach. International Journal of Environmental Research and Public Health. 2021; 18(15):8110. https://doi.org/10.3390/ijerph18158110

Chicago/Turabian Style

Cugnata, Federica, Silvia Salini, and Elena Siletti. 2021. "Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach" International Journal of Environmental Research and Public Health 18, no. 15: 8110. https://doi.org/10.3390/ijerph18158110

APA Style

Cugnata, F., Salini, S., & Siletti, E. (2021). Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach. International Journal of Environmental Research and Public Health, 18(15), 8110. https://doi.org/10.3390/ijerph18158110

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