This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1349) Depression and Mood Disorders; Suicide Prevention (1363) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 18, No 2 (2016): February

Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries

Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries

Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries

Authors of this article:

Donghyun Lee1 Author Orcid Image ;   Hojun Lee2 Author Orcid Image ;   Munkee Choi1 Author Orcid Image
Article Authors Cited by (14) Tweetations (11) Metrics

Journals

  1. Lee G, Chong Y, Kim B. Differential Effect of Time Perspective on Global Superiority. Journal of Social Science 2019;30(2):15 View
  2. Adler N, Cattuto C, Kalimeri K, Paolotti D, Tizzoni M, Verhulst S, Yom-Tov E, Young A. How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study. Journal of Medical Internet Research 2019;21(1):e10179 View
  3. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  4. Lee D, Kang S, Shin J. Determinants of Pro-Environmental Consumption: Multicountry Comparison Based upon Big Data Search. Sustainability 2017;9(2):183 View
  5. Sueki H, Ito J. Appropriate Targets for Search Advertising as Part of Online Gatekeeping for Suicide Prevention. Crisis 2018;39(3):197 View
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  8. AYAN B. YENİ BİR VERİ KAYNAĞI OLARAK GOOGLE TRENDS: GELECEK YÖNELİMİ ENDEKSİ İLE İLGİLİ BİR DEĞERLENDİRME / Google Trends As A New Data Source: An Evaluation Of The Future Orientation Index. Uluslararası Ekonomi İşletme ve Politika Dergisi 2020;4(1):61 View
  9. Lee D, Kang S, Shin J. Using Deep Learning Techniques to Forecast Environmental Consumption Level. Sustainability 2017;9(10):1894 View
  10. Arendt F. Suicide rates and information seeking via search engines: A cross-national correlational approach. Death Studies 2018;42(8):508 View
  11. Ulanja M, Lyons C, Ketende S, Stahlman S, Diouf D, Kouamé A, Ezouatchi R, Bamba A, Drame F, Liestman B, Baral S. The relationship between depression and sexual health service utilization among men who have sex with men (MSM) in Côte d'Ivoire, West Africa. BMC International Health and Human Rights 2019;19(1) View
  12. Taira K, Hosokawa R, Itatani T, Fujita S. Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model. JMIR Public Health and Surveillance 2021;7(12):e34016 View
  13. Tang H, Qiu Y, Guo Y, Liu J. Comparison of Periodic Behavior of Consumer Online Searches for Restaurants in the U.S. and China Based on Search Engine Data. IEEE Access 2018;6:34109 View
  14. Arai T, Tsubaki H, Wakano A, Shimizu Y. Association Between School-Related Google Trends Search Volume and Suicides Among Children and Adolescents in Japan During 2016-2020: Retrospective Observational Study With a Time-Series Analysis. Journal of Medical Internet Research 2024;26:e51710 View