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Measuring Idiosyncratic Happiness Through the Analysis of Twitter: An Application to the Italian Case

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  • Luigi Curini
  • Stefano Iacus
  • Luciano Canova

Abstract

We examine messages posted by Italian users on the social networking service Twitter to investigate idiosyncratic shocks of happiness. Contrary to questionnaires, analyzing social media allows to nowcast happiness in real-time, including mapping its fluctuation due to the occurrence of extemporaneous facts. Our data set comprises over 43 millions of tweets posted on a daily basis in all the 110 Italian provinces and collected during 2012. In measuring happiness, we construct an original index using innovative statistical techniques to quantify the level of happiness at the provincial level related to a specific amount of tweets. We then explore the determinants of happiness in our sample. Static variables such as the overall quality of institutions seem to matter only marginally in affecting the average level of happiness of the Italian provinces. On the contrary, meteorological variables and events related to specific days, such as the variability of the spread between German and Italian Bonds or the payday, have the largest impact. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Luigi Curini & Stefano Iacus & Luciano Canova, 2015. "Measuring Idiosyncratic Happiness Through the Analysis of Twitter: An Application to the Italian Case," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 121(2), pages 525-542, April.
  • Handle: RePEc:spr:soinre:v:121:y:2015:i:2:p:525-542
    DOI: 10.1007/s11205-014-0646-2
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    2. Obschonka, Martin & Lee, Neil & Rodríguez-Pose, Andrés & Eichstaedt, johannes Christopher & Ebert, Tobias, 2018. "Big Data, artificial intelligence and the geography of entrepreneurship in the United States," OSF Preprints c62tn, Center for Open Science.
    3. Tiziana Carpi & Airo Hino & Stefano Maria Iacus & Giuseppe Porro, 2020. "On a Japanese Subjective Well-Being Indicator Based on Twitter data," Papers 2012.14372, arXiv.org.
    4. Ghasem Javadi & Mohammad Taleai, 0. "Integration of User Generated Geo-contents and Official Data to Assess Quality of Life in Intra-national Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 0, pages 1-31.
    5. Tiziana Carpi & Airo Hino & Stefano Maria Iacus & Giuseppe Porro, 2021. "Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study," Papers 2101.07695, arXiv.org.
    6. Canova, Luciano & Nicolini, Marcella, 2019. "Online price search across desktop and mobile devices: Evidence on cyberslacking and weather effects," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 32-39.
    7. Ghasem Javadi & Mohammad Taleai, 2020. "Integration of User Generated Geo-contents and Official Data to Assess Quality of Life in Intra-national Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 205-235, November.
    8. Castellani, Marco, 2019. "Does culture matter for the economic performance of countries? An overview of the literature," Journal of Policy Modeling, Elsevier, vol. 41(4), pages 700-717.
    9. Martin Obschonka & Neil Lee & Andrés Rodríguez-Pose & Johannes C. Eichstaedt & Tobias Ebert, 2020. "Big data methods, social media, and the psychology of entrepreneurial regions: capturing cross-county personality traits and their impact on entrepreneurship in the USA," Small Business Economics, Springer, vol. 55(3), pages 567-588, October.
    10. Stefano Marchetti & Caterina Giusti & Monica Pratesi, 2016. "The use of Twitter data to improve small area estimates of households’ share of food consumption expenditure in Italy [Die Nutzung von Twitter Daten um die Small Area Schätzungen vom Ausgabenanteil," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 79-93, October.

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