Since 2012, driven by the desire to propose a subjective well-being index (SWBI) comple- mentary to the traditional measures, with high time and space frequency, our team evalu- ates, analysing Twitter data, a composite index that captures various aspects and dimen- sions 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 (SWBIITA), that we display here, audits the Italian sub- jective 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 SWBIITA 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 emphasiz- ing novelties, similarities and differences.

An Italian Composite Subjective Well‐Being Index: The Voice of Twitter Users from 2012 to 2017

Giuseppe PORRO;
2022-01-01

Abstract

Since 2012, driven by the desire to propose a subjective well-being index (SWBI) comple- mentary to the traditional measures, with high time and space frequency, our team evalu- ates, analysing Twitter data, a composite index that captures various aspects and dimen- sions 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 (SWBIITA), that we display here, audits the Italian sub- jective 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 SWBIITA 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 emphasiz- ing novelties, similarities and differences.
2022
2020
https://link.springer.com/article/10.1007/s11205-020-02319-6
Big data; Composite indicators; Quality of life; Sentiment analysis; Social network;
Maria IACUS, Stefano; Porro, Giuseppe; Salini, Silvia; Siletti, Elena
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2087616
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