Low validity of Google Trends for behavioral forecasting of national suicide rates

Recent research suggests that search volumes of the most popular search engine worldwide, Google, provided via Google Trends, could be associated with national suicide rates in the USA, UK, and some Asian countries. However, search volumes have mostly been studied in an ad hoc fashion, without controls for spurious associations. This study evaluated the validity and utility of Google Trends search volumes for behavioral forecasting of suicide rates in the USA, Germany, Austria, and Switzerland. Suicide-related search terms were systematically collected and respective Google Trends search volumes evaluated for availability. Time spans covered 2004 to 2010 (USA, Switzerland) and 2004 to 2012 (Germany, Austria). Temporal associations of search volumes and suicide rates were investigated with time-series analyses that rigorously controlled for spurious associations. The number and reliability of analyzable search volume data increased with country size. Search volumes showed various temporal associations with suicide rates. However, associations differed both across and within countries and mostly followed no discernable patterns. The total number of significant associations roughly matched the number of expected Type I errors. These results suggest that the validity of Google Trends search volumes for behavioral forecasting of national suicide rates is low. The utility and validity of search volumes for the forecasting of suicide rates depend on two key assumptions (“the population that conducts searches consists mostly of individuals with suicidal ideation”, “suicide-related search behavior is strongly linked with suicidal behavior”). We discuss strands of evidence that these two assumptions are likely not met. Implications for future research with Google Trends in the context of suicide research are also discussed.

[1]  Florian Arendt,et al.  Optimizing Online Suicide Prevention: A Search Engine-Based Tailored Approach , 2017, Health communication.

[2]  A. Zeileis,et al.  zoo: S3 Infrastructure for Regular and Irregular Time Series , 2005, math/0505527.

[3]  Elizabeth Cummings Trends in mental health googling , 2009 .

[4]  H. Möller,et al.  Suizidalität und Internet , 2006, Der Nervenarzt.

[5]  P. Recupero,et al.  Googling suicide: surfing for suicide information on the Internet. , 2008, The Journal of clinical psychiatry.

[6]  A. Hagihara,et al.  Internet suicide searches and the incidence of suicide in young people in Japan , 2011, European Archives of Psychiatry and Clinical Neuroscience.

[7]  T. Niederkrotenthaler,et al.  Surfing for suicide methods and help: content analysis of websites retrieved with search engines in Austria and the United States. , 2014, The Journal of clinical psychiatry.

[8]  D. Lester,et al.  Using google searches on the internet to monitor suicidal behavior. , 2013, Journal of affective disorders.

[9]  I. Kawachi,et al.  Rethinking Suicide Surveillance , 2016 .

[10]  Diana Adler,et al.  Using Multivariate Statistics , 2016 .

[11]  David Gunnell,et al.  Surveillance of Australian Suicidal Behaviour Using the Internet? , 2011, The Australian and New Zealand journal of psychiatry.

[12]  J. Brownstein,et al.  Tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance. , 2011, American journal of preventive medicine.

[13]  L. Harlow,et al.  Big data in psychology: Introduction to the special issue. , 2016, Psychological methods.

[14]  Johan Bollen,et al.  Quantifying the effects of online bullishness on international financial markets , 2015 .

[15]  J. Brownstein,et al.  Digital disease detection--harnessing the Web for public health surveillance. , 2009, The New England journal of medicine.

[16]  M. McCarthy,et al.  Internet monitoring of suicide risk in the population. , 2010, Journal of affective disorders.

[17]  Ś. Sen,et al.  Use of Google Insights for Search to track seasonal and geographic kidney stone incidence in the United States. , 2011, Urology.

[18]  Alexander Aue,et al.  Applied Time Series Analysis , 2010 .

[19]  Matthew Mohebbi,et al.  Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic , 2011, PloS one.

[20]  Cécile Viboud,et al.  Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales , 2013, PLoS Comput. Biol..

[21]  K. Fu,et al.  Accessing Suicide-Related Information on the Internet: A Retrospective Observational Study of Search Behavior , 2013, Journal of medical Internet research.

[22]  David Gunnell,et al.  Internet searches for a specific suicide method follow its high-profile media coverage. , 2011, The American journal of psychiatry.

[23]  Matthew L. Martinich Pew Research Center , 2019, Encyclopedia of Latin American Religions.

[24]  K. Beullens,et al.  The Swine Flu Emergency Department: The Relationship Between Media Attention for the Swine Flu and Registrations in an Emergency Medicine Unit , 2014, Prehospital and Disaster Medicine.

[25]  H. Sueki Does the volume of Internet searches using suicide‐related search terms influence the suicide death rate: Data from 2004 to 2009 in Japan , 2011, Psychiatry and clinical neurosciences.

[26]  B. Tabachnick,et al.  Using multivariate statistics, 5th ed. , 2007 .

[27]  Gunther Eysenbach,et al.  Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance , 2006, AMIA.

[28]  C. Peng,et al.  Association of Internet search trends with suicide death in Taipei City, Taiwan, 2004-2009. , 2011, Journal of affective disorders.

[29]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[30]  Jörg Rech,et al.  Discovering trends in software engineering with google trend , 2007, SOEN.

[31]  Melonie P. Heron Deaths: leading causes for 2008. , 2010, National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System.

[32]  M. Vicente,et al.  Monitoring influenza activity in Europe with Google Flu Trends: comparison with the findings of sentinel physician networks - results for 2009-10. , 2010, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[33]  Guido Caldarelli,et al.  Web Search Queries Can Predict Stock Market Volumes , 2011, PloS one.

[34]  A. Dugas,et al.  Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. , 2011, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[35]  Nicola Döring,et al.  Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften , 2016 .

[36]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[37]  Kung-Sik Chan,et al.  Time Series Analysis: With Applications in R , 2010 .

[38]  Wendy W. Chapman,et al.  Analysis of Web Access Logs for Surveillance of Influenza , 2004, MedInfo.

[39]  H. Varian,et al.  Predicting the Present with Google Trends , 2012 .

[40]  H. Varian,et al.  Predicting the Present with Google Trends , 2009 .

[41]  M. Santillana,et al.  What can digital disease detection learn from (an external revision to) Google Flu Trends? , 2014, American journal of preventive medicine.

[42]  Adekola O. Alao,et al.  Cybersuicide: Review of the Role of the Internet on Suicide , 2006, Cyberpsychology Behav. Soc. Netw..

[43]  C. Lahmann,et al.  Schleudertrauma und Werther-Effekt – das Potenzial von Google Insights for Search für die medizinische Forschung und öffentliche Gesundheit , 2011 .

[44]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[45]  John Kalafat,et al.  An evaluation of crisis hotline outcomes. Part 2: Suicidal callers. , 2007, Suicide & life-threatening behavior.

[46]  Declan Butler,et al.  When Google got flu wrong , 2013, Nature.

[47]  Amanda Aitken Ba Pgce DipCouns Suicide and the internet , 2009 .

[48]  Juyoung Song,et al.  Psychological and Social Factors Affecting Internet Searches on Suicide in Korea: A Big Data Analysis of Google Search Trends , 2013, Yonsei medical journal.

[49]  J. V. Dijk,et al.  The Digital Divide in Europe , 2007 .

[50]  David M. Pennock,et al.  Using internet searches for influenza surveillance. , 2008, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[51]  David Gunnell,et al.  Exposure to, and searching for, information about suicide and self-harm on the Internet: Prevalence and predictors in a population based cohort of young adults , 2015, Journal of affective disorders.

[52]  T. Bruckner,et al.  Google searches for suicide and risk of suicide. , 2014, Psychiatric services.

[53]  E. Seifritz,et al.  Understanding weekly cycles in suicide: an analysis of Austrian and Swiss data over 40 years , 2014, Epidemiology and Psychiatric Sciences.

[54]  Christian Köhler,et al.  What is the prevalence of health-related searches on the World Wide Web? Qualitative and quantitative analysis of search engine queries on the Internet , 2003, AMIA.

[55]  Hajime Sueki Ma Does the volume of Internet searches using suicide-related search terms influence the suicide death rate: Data from 2004 to 2009 in Japan , 2011 .

[56]  VIviane Brunne,et al.  Active ageing and solidarity between generations in Europe and beyond. A view from the United Nations Economic Commission for Europe , 2013 .

[57]  H Eugene Stanley,et al.  Complex dynamics of our economic life on different scales: insights from search engine query data , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[58]  Matthias Bopp,et al.  Methods of suicide: international suicide patterns derived from the WHO mortality database. , 2008, Bulletin of the World Health Organization.

[59]  B. Barraclough,et al.  Excess mortality of mental disorder , 1998, British Journal of Psychiatry.

[60]  B. Breyer,et al.  Use of Google in study of noninfectious medical conditions. , 2010, Epidemiology.

[61]  A. Flahault,et al.  More Diseases Tracked by Using Google Trends , 2009, Emerging infectious diseases.

[62]  Ladislav Kristoufek,et al.  Estimating suicide occurrence statistics using Google Trends , 2016, EPJ Data Science.