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Assessing Social Interest in Burnout Using Google Trends Data

Author

Listed:
  • Ana Maria Aguilera

    (University of Granada)

  • Francesca Fortuna

    (“G. d’ Annunzio” University)

  • Manuel Escabias

    (University of Granada)

  • Tonio Di Battista

    (“G. d’ Annunzio” University)

Abstract

Burnout is a serious problem in modern society and early detection methods are needed to successfully handled its multiple effects. The latter refer to working well-being, as well as to the affective, psychological, physiological, and behavioral well-being of workers. However, in many countries official statistics on this topic are not available. For this reason, we propose to use Google Trends data as proxies for the interest in burnout and to analyze them through the functional data analysis approach. The latter allows to address the so-called ‘curse of dimensionality’ of big data, enabling an effective statistical analysis when the number of variables exceeds the number of observations. Under this framework, the functional analysis of variance (FANOVA) model is used for testing a macro geographic area effect on search queries for the keyword “burnout” in Italy. The estimation of the FANOVA model is proposed in a finite dimensional space generated by a basis function representation. Thus, the functional model is reduced to a MANOVA model on the basis coefficients.

Suggested Citation

  • Ana Maria Aguilera & Francesca Fortuna & Manuel Escabias & Tonio Di Battista, 2021. "Assessing Social Interest in Burnout Using Google Trends Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 587-599, August.
    • Handle: RePEc:spr:soinre:v:156:y:2021:i:2:d:10.1007_s11205-019-02250-5
    DOI: 10.1007/s11205-019-02250-5
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    References listed on IDEAS

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    1. Ferraty, Frederic & Vieu, Philippe & Viguier-Pla, Sylvie, 2007. "Factor-based comparison of groups of curves," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4903-4910, June.
    2. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    3. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2004. "An anova test for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 111-122, August.
    4. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    5. Enrico di Bella & Lucia Leporatti & Filomena Maggino, 2018. "Big Data and Social Indicators: Actual Trends and New Perspectives," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(3), pages 869-878, February.
    6. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
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    Cited by:

    1. Christian Acal & Ana M. Aguilera, 2023. "Basis expansion approaches for functional analysis of variance with repeated measures," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 291-321, June.

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