This study presents for the first time the SWB-J index, a subjective well-being indicator for Japan based on Twitter data. The index is composed by eight dimensions of subjective well-being and is estimated relying on Twitter data by using human supervised sentiment analysis. The index is then compared with the analogous SWB-I index for Italy in order to verify possible analogies and cultural differences. Further, through structural equation models, we investigate the relationship between economic and health conditions of the country and the well-being latent variable and illustrate how this latent dimension affects the SWB-J and SWB-I indicators. It turns out that, as expected, economic and health welfare is only one aspect of the multidimensional well-being that is captured by the Twitter-based indicator.

A Japanese Subjective Well-Being Indicator Based on Twitter Data

PORRO, Giuseppe
In corso di stampa

Abstract

This study presents for the first time the SWB-J index, a subjective well-being indicator for Japan based on Twitter data. The index is composed by eight dimensions of subjective well-being and is estimated relying on Twitter data by using human supervised sentiment analysis. The index is then compared with the analogous SWB-I index for Italy in order to verify possible analogies and cultural differences. Further, through structural equation models, we investigate the relationship between economic and health conditions of the country and the well-being latent variable and illustrate how this latent dimension affects the SWB-J and SWB-I indicators. It turns out that, as expected, economic and health welfare is only one aspect of the multidimensional well-being that is captured by the Twitter-based indicator.
In corso di stampa
2022
https://academic.oup.com/ssjj/advance-article/doi/10.1093/ssjj/jyac002/6564921
subjective well-being, Japan, Twitter data, sentiment analysis, structural equation modeling
Carpi, Tiziana; Hino, Airo; Iacus, Stefano Maria; Porro, Giuseppe
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2132404
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