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

Joana M. Barros, Ruth Melia, Kady Francis, John Bogue, Mary O’Sullivan, Karen Young, Rebecca A. Bernert, Dietrich Rebholz-Schuhmann and Jim Duggan
Additional contact information
Joana M. Barros: Insight Centre for Data Analytics, NUI Galway, H91 AEX4 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
Dietrich Rebholz-Schuhmann: ZB MED, University of Cologne, Gleueler Str. 60, 50931 Cologne, Germany
Jim Duggan: School of Computer Science, National University of Ireland Galway, Galway, Ireland

IJERPH, 2019, vol. 16, issue 17, 1-18

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.

Keywords: suicide; Google Trends; forecasting; autoregression; neural networks; Ireland (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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