Abstract
Since 2012, driven by the desire to propose a subjective well-being index (SWBI) complementary to the traditional measures, with high time and space frequency, our team evaluates, analysing Twitter data, a composite index that captures various aspects and dimensions of individual and collective life. The SWBI is a multidimensional indicator whose components were inspired by the dimensions adopted for the Happy Planet Index provided by the New Economic Foundation. In detail, it consists of eight dimensions that describe three different areas: personal well-being, social well-being and well-being at work. The Italian subjective well-being index (\(\hbox {SWBI}_{{ITA}}\)), that we display here, audits the Italian subjective well-being revealed by tweets acquired via the public Twitter API, written in the Italian language, and posted from Italy from January 2012 to December 2017. Around 1–5% of the data includes geo-referenced information, which allows us to provide an index at local level. The Twitter data analysis is carried on with a human supervised sentiment analysis method, the Integrated Sentiment Analysis (iSA) algorithm. In this work, after a weighting procedure adopted to partially overcome the selection bias caused by the use of data from social network, we describe the \(\hbox {SWBI}_{{ITA}}\) dimensions in the considered period at the regional level. Moreover, for some dimensions, for which a similar currently available measure provided by Italian official statistics exists, comparisons are proposed emphasizing novelties, similarities and differences.
Similar content being viewed by others
Notes
Penetration data from the We Are Social and Hootsuite’s report (“Digital in 2019”, Jan 2019; available at http://wearesocial.com): looking at the world population 57% (+9.1%) has Internet access, and 45% (+9%) has a social media account and makes an active use of it, while in Italy these percentages are respectively 92% (+27%) and 59% (+2.9%). Annual digital growth from January 2018 to January 2019 in brackets.
Notice that Valle d’Aosta region has been dropped from the analysis because, considering that it consists of a single province, the proposed approach is not applicable.
References
Ceron, A., Curini, L., & Iacus, S. M. (2016). iSA: A fast, scalable and accurate algorithm for sentiment analysis of social media content. Information Sciences, 367–368, 105–124. https://doi.org/10.1016/j.ins.2016.05.052.
Cooper, D., & Greenaway, M. (2015). Non-probability survey sampling in official statistics. Tech. rep., Office for National Statistics—Methodology Working Paper Series N.4.
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.
Deaton, A. (2012). The financial crisis and the well-being of America (Vol. 10, pp. 343–368). Chicago, IL: University of Chicago Press.
Feddersen, J., Metcalfe, R., & Wooden, M. (2016). Subjective wellbeing: Why weather matters. Journal of the Royal Statistical Society: Series A (Statistics in Society), 179(1), 203–228.
Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161.
Hofacker, C. F., Malthouse, E. C., & Sultan, F. (2016). Big data and consumer behavior: Imminent opportunities. Journal of Consumer Marketing, 33(2), 89–97.
Iacus, S. M., Porro, G., Salini, S., & Siletti, E. (2015). Social networks, happiness and health: from sentiment analysis to a multidimensional indicator of subjective well-being. ArXiv e-prints arxiv, 1512, 01569.
Iacus, S. M., Porro, G., Salini, S., & Siletti, E. (2017). How to exploit big data from social networks: A subjective well-being indicator via Twitter. In: A. Petrucci, R. Verde (Eds.), SIS 2017. Statistics and data science: new challenges, new generations. Proceedings of the Conference of the Italian Statistical Society (pp. 537–542). Firenze: Firenze University Press.
Iacus, S. M., Porro, G., Salini, S., & Siletti, E. (forthcoming) Controlling for selection bias in social media indicators through official statistics: A proposal. Journal of Official Statistics.
ISTAT. (2017). La soddisfazione dei cittadini per le condizioni di vita. Tech. rep., ISTA, https://www.istat.it/it/files//2018/01/Soddisfazione-cittadini.pdf.
Kahneman, D., & Krueger, A. B. (2006). Developments in the measurement of subjective well-being. Journal of Economic Perspectives, 20(1), 3–24.
Kwong, B. M., McPherson, S. M., Shibata, J. F. A., & Zee, O. T. (2012). Facebook: Data mining the world’s largest focus group. Graziadia Business Review, 15, 1–8.
New Economics Foundation. (2012). The Happy Planet Index: 2012 report. A global index of sustainable well-being. Tech. rep., New Economics Foundation.
Rosembaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Schwarz, N. (1999). Self-reports: How the questions shape the answers. American Psychologist, 54(2), 93–105.
Schwarz, N., & Strack, F. (1999). Reports of subjective well-being: Judgmental processes and their methodological implications. Well-Being: The Foundations of Hedonic Psychology, 7, 61–84.
Stiglitz, J., Sen, A., & Fitoussi, J. P. (2009). Report by the commission on the measurement of economic performance and social progress. Tech. rep., INSEE.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Iacus, S.M., Porro, G., Salini, S. et al. An Italian Composite Subjective Well-Being Index: The Voice of Twitter Users from 2012 to 2017. Soc Indic Res 161, 471–489 (2022). https://doi.org/10.1007/s11205-020-02319-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-020-02319-6