HOW ? By processing hundreds of billions of individual searches from 5 years (2003-2008) of Google search logs To track in...
PREVIOUS ATTEMPS  <ul><li>Queries to a Swedish medical website </li></ul><ul><li>Visitors to pages on a U.S. health websit...
HOW EXACTLY ? <ul><li>Automated method of selecting queries: </li></ul><ul><li>50 million queries separately </li></ul><ul...
MEAN CORRELATION 0.97
 
Texte
Texte
 
J. S. Brownstein, C. Freifeld, C. Madoff, 2009 “ Digital Disease Detection” New England Journal of Medicine 360, no. 21
Jurgen Doornik 2009 “ Improving the Timeliness of Data on Influenza-like Illnesses using Google Search Data” (unpublished)
Hyunyoung Choi and Hal Varian, 2009 “Predicting the Present with Google Trends”  http://googleresearch.blogspot.com
N. Askitas and K. Zimmermann, 2009 “Google Econometrics and Unemployment Forecasting.”
G. K., Webb, 2009 &quot;Forecasting U.S. Home Foreclosures with an Index of Internet Keyword Searches&quot; In Value Creat...
J. Azar, 2009 &quot;Electric Cars and Oil Prices&quot; SSRN eLibrary   
Laura Granka 2009 &quot;Inferring the Public Agenda from Implicit Query Data&quot; Proceeding SIGIR 2009
M. Scharkow / J. Vogelgesang, 2009 &quot;Google Insights for Search: A Methodological Innovation in the Study of the Publi...
Yair Shimshoni Niv Efron Yossi Matias, 2009 &quot;On the Predictability of Search Trends&quot; Google, Israel Labs
R. Karthik, A. Rachakonda, S. Srinivasa, 2008 &quot;Query Heartbeat&quot; In proceeding of COMAD
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DIM 11.09 - Tommaso Venturini, médialab SciencesPo

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DIM 11.09 - Tommaso Venturini, médialab SciencesPo

  1. 2. HOW ? By processing hundreds of billions of individual searches from 5 years (2003-2008) of Google search logs To track influenza-like illness in a population.
  2. 3. PREVIOUS ATTEMPS <ul><li>Queries to a Swedish medical website </li></ul><ul><li>Visitors to pages on a U.S. health website </li></ul><ul><li>User clicks on a advertisement in Canada </li></ul><ul><li>A set of Yahoo search queries </li></ul>
  3. 4. HOW EXACTLY ? <ul><li>Automated method of selecting queries: </li></ul><ul><li>50 million queries separately </li></ul><ul><li>Sets of N top scoring queries </li></ul><ul><li>Combining the N=45 highest-scoring queries was found to obtain the best fit. </li></ul>
  4. 5. MEAN CORRELATION 0.97
  5. 7. Texte
  6. 8. Texte
  7. 10. J. S. Brownstein, C. Freifeld, C. Madoff, 2009 “ Digital Disease Detection” New England Journal of Medicine 360, no. 21
  8. 11. Jurgen Doornik 2009 “ Improving the Timeliness of Data on Influenza-like Illnesses using Google Search Data” (unpublished)
  9. 12. Hyunyoung Choi and Hal Varian, 2009 “Predicting the Present with Google Trends” http://googleresearch.blogspot.com
  10. 13. N. Askitas and K. Zimmermann, 2009 “Google Econometrics and Unemployment Forecasting.”
  11. 14. G. K., Webb, 2009 &quot;Forecasting U.S. Home Foreclosures with an Index of Internet Keyword Searches&quot; In Value Creation in E-Business Management
  12. 15. J. Azar, 2009 &quot;Electric Cars and Oil Prices&quot; SSRN eLibrary  
  13. 16. Laura Granka 2009 &quot;Inferring the Public Agenda from Implicit Query Data&quot; Proceeding SIGIR 2009
  14. 17. M. Scharkow / J. Vogelgesang, 2009 &quot;Google Insights for Search: A Methodological Innovation in the Study of the Public Agenda? » DGPuK Conference
  15. 18. Yair Shimshoni Niv Efron Yossi Matias, 2009 &quot;On the Predictability of Search Trends&quot; Google, Israel Labs
  16. 19. R. Karthik, A. Rachakonda, S. Srinivasa, 2008 &quot;Query Heartbeat&quot; In proceeding of COMAD

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