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The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland

Author

Listed:
  • Joana M. Barros

    (Insight Centre for Data Analytics, NUI Galway, H91 AEX4 Galway, Ireland
    School of Computer Science, National University of Ireland Galway, Galway, Ireland)

  • Ruth Melia

    (Psychology Department, Health Service Executive MidWest, Ennis, Ireland)

  • Kady Francis

    (Psychology Department, Health Service Executive Dublin Mid Leinster, Longford, Ireland)

  • John Bogue

    (School of Psychology, National University of Ireland Galway, H91 EV56 Galway, Ireland)

  • Mary O’Sullivan

    (Suicide Prevention Resource Office, Health Service Executive West, Galway, Ireland)

  • Karen Young

    (School of Computer Science, National University of Ireland Galway, Galway, Ireland)

  • Rebecca A. Bernert

    (Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305-5717, USA
    Indicates Co-Senior Authorship.)

  • Dietrich Rebholz-Schuhmann

    (ZB MED, University of Cologne, Gleueler Str. 60, 50931 Cologne, Germany
    Indicates Co-Senior Authorship.)

  • Jim Duggan

    (School of Computer Science, National University of Ireland Galway, Galway, Ireland
    Indicates Co-Senior Authorship.)

Abstract

Annual suicide figures are critical in identifying trends and guiding research, yet challenges arising from significant lags in reporting can delay and complicate real-time interventions. In this paper, we utilized Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland between 2004 and 2015. Official suicide rates are recorded by the Central Statistics Office in Ireland. While similar investigations using Google trends data have been carried out in other jurisdictions (e.g., United Kingdom, United Stated of America), such research had not yet been completed in Ireland. We compiled a collection of suicide- and depression-related search terms suggested by Google Trends and manually sourced from the literature. Monthly search rate terms at different lags were compared with suicide occurrences to determine the degree of correlation. Following two approaches based on vector autoregression and neural network autoregression, we achieved mean absolute error values between 4.14 and 9.61 when incorporating search query data, with the highest performance for the neural network approach. The application of this process to United Kingdom suicide and search query data showed similar results, supporting the benefit of Google Trends, neural network approach, and the applied search terms to forecast suicide risk increase. Overall, the combination of societal data and online behavior provide a good indication of societal risks; building on past research, our improvements led to robust models integrating search query and unemployment data for suicide risk forecasting in Ireland.

Suggested Citation

  • Joana M. Barros & Ruth Melia & Kady Francis & John Bogue & Mary O’Sullivan & Karen Young & Rebecca A. Bernert & Dietrich Rebholz-Schuhmann & Jim Duggan, 2019. "The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland," IJERPH, MDPI, vol. 16(17), pages 1-18, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:17:p:3201-:d:263272
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    References listed on IDEAS

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    1. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. Pfaff, Bernhard, 2008. "VAR, SVAR and SVEC Models: Implementation Within R Package vars," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i04).
    4. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    5. Ulrich S Tran & Rita Andel & Thomas Niederkrotenthaler & Benedikt Till & Vladeta Ajdacic-Gross & Martin Voracek, 2017. "Low validity of Google Trends for behavioral forecasting of national suicide rates," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-26, August.
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    7. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    8. Hyekyung Woo & Youngtae Cho & Eunyoung Shim & Kihwang Lee & Gilyoung Song, 2015. "Public Trauma after the Sewol Ferry Disaster: The Role of Social Media in Understanding the Public Mood," IJERPH, MDPI, vol. 12(9), pages 1-10, September.
    9. David S Fink & Julian Santaella-Tenorio & Katherine M Keyes, 2018. "Increase in suicides the months after the death of Robin Williams in the US," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-12, February.
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