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.

Deepening well-being evaluation with different data sources: A bayesian networks approach / F. Cugnata, S. Salini, E. Siletti. - In: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. - ISSN 1661-7827. - 18:15(2021 Aug), pp. 8110.1-8110.10. [10.3390/ijerph18158110]

Deepening well-being evaluation with different data sources: A bayesian networks approach

S. Salini
Secondo
;
2021-08

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.
Bayesian networks; Big data; Life satisfaction; Sentiment analysis (list three to ten); Well-being; Geography; Humans; Italy; Surveys and Questionnaires; Bayes Theorem
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/867078
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