tetano
Editor, Senior Moderator
Science 14 March 2014:
Vol. 343 no. 6176 pp. 1203-1205
DOI: 10.1126/science.1248506
Policy Forum
Big Data
The Parable of Google Flu: Traps in Big Data Analysis
David Lazer1,2,*,
Ryan Kennedy1,3,4,
Gary King3,
Alessandro Vespignani5,6,3
1Lazer Laboratory, Northeastern University, Boston, MA 02115, USA.
2Harvard Kennedy School, Harvard University, Cambridge, MA 02138, USA.
3Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA.
4University of Houston, Houston, TX 77204, USA.
5Laboratory for the Modeling of Biological and Sociotechnical Systems, Northeastern University, Boston, MA 02115, USA.
6Institute for Scientific Interchange Foundation, Turin, Italy.
↵*Corresponding author. E-mail: d.lazer{at}neu.edu.
In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States (1, 2). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data (3, 4), what lessons can we draw from this error?
http://www.sciencemag.org/content/343/6176/1203
Vol. 343 no. 6176 pp. 1203-1205
DOI: 10.1126/science.1248506
Policy Forum
Big Data
The Parable of Google Flu: Traps in Big Data Analysis
David Lazer1,2,*,
Ryan Kennedy1,3,4,
Gary King3,
Alessandro Vespignani5,6,3
1Lazer Laboratory, Northeastern University, Boston, MA 02115, USA.
2Harvard Kennedy School, Harvard University, Cambridge, MA 02138, USA.
3Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA.
4University of Houston, Houston, TX 77204, USA.
5Laboratory for the Modeling of Biological and Sociotechnical Systems, Northeastern University, Boston, MA 02115, USA.
6Institute for Scientific Interchange Foundation, Turin, Italy.
↵*Corresponding author. E-mail: d.lazer{at}neu.edu.
In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States (1, 2). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data (3, 4), what lessons can we draw from this error?
http://www.sciencemag.org/content/343/6176/1203