Published on in Vol 21, No 1 (2019): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10179, first published .
How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study

How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study

How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study

Journals

  1. Jimenez A, Santed-Germán M, Ramos V. Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study. JMIR Public Health and Surveillance 2020;6(2):e10919 View
  2. Kalimeri K, Beiró M, Bonanomi A, Rosina A, Cattuto C. Traditional versus Facebook-based surveys: Evaluation of biases in self-reported demographic and psychometric information. Demographic Research 2020;42:133 View
  3. Chen Y, He G, Chen B, Wang S, Ju G, Ge T. The association between PM2.5 exposure and suicidal ideation: a prefectural panel study. BMC Public Health 2020;20(1) View
  4. Voukelatou V, Gabrielli L, Miliou I, Cresci S, Sharma R, Tesconi M, Pappalardo L. Measuring objective and subjective well-being: dimensions and data sources. International Journal of Data Science and Analytics 2021;11(4):279 View
  5. Areán P, Pratap A, Hsin H, Huppert T, Hendricks K, Heagerty P, Cohen T, Bagge C, Comtois K. Perceived Utility and Characterization of Personal Google Search Histories to Detect Data Patterns Proximal to a Suicide Attempt in Individuals Who Previously Attempted Suicide: Pilot Cohort Study. Journal of Medical Internet Research 2021;23(5):e27918 View
  6. Hardinghaus M, Nieland S. Assessing cyclists’ routing preferences by analyzing extensive user setting data from a bike-routing engine. European Transport Research Review 2021;13(1) View
  7. 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
  8. Kruzan K, Fitzsimmons-Craft E, Dobias M, Schleider J, Pratap A. Developing, Deploying, and Evaluating Digital Mental Health Interventions in Spaces of Online Help- and Information-Seeking. Procedia Computer Science 2022;206:6 View
  9. Sartirano D, Kalimeri K, Cattuto C, Delamónica E, Garcia-Herranz M, Mockler A, Paolotti D, Schifanella R. Strengths and limitations of relative wealth indices derived from big data in Indonesia. Frontiers in Big Data 2023;6 View
  10. Luca M, Campedelli G, Centellegher S, Tizzoni M, Lepri B. Crime, inequality and public health: a survey of emerging trends in urban data science. Frontiers in Big Data 2023;6 View
  11. SEKARA V, KARSAI M, MORO E, KIM D, DELAMONICA E, CEBRIAN M, LUENGO-OROZ M, JIMÉNEZ R, GARCIA-HERRANZ M. THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENT. Advances in Complex Systems 2024;27(03) View

Books/Policy Documents

  1. Young A, Verhulst S. The Palgrave Encyclopedia of Interest Groups, Lobbying and Public Affairs. View
  2. Urbinati A, Kalimeri K, Bonanomi A, Rosina A, Cattuto C, Paolotti D. Social Informatics. View
  3. Young A, Verhulst S. The Palgrave Encyclopedia of Interest Groups, Lobbying and Public Affairs. View
  4. Mejova Y. Handbook of Computational Social Science for Policy. View