Much has been made of David Lazer’s finding that Google’s Flu Trends tracker seriously missed the mark in its measurement of flu activity for 2012-2013—and in previous years, too.
For those who don’t know, Flu Trends monitors Google search behaviors to identify regions where searches related to flu-like symptoms are spiking.In spite of Flu Trend’s notable misstep, Lazer still believes in the power of marrying health and social data.
In discussing the results of his study, he has maintained Google Flu is “a terrific” idea—one that just needs some refining. I agree.And, earlier this month, Nicholas Christakis, several other colleagues, and I—with funding from the Robert Wood Johnson Foundation—published a new method offering one such refinement.
Our paper shows that, in a given social network (in this study’s case, Twitter), a sample of its most connected, central individuals can hold significant predictive power.
We call this potentially powerful group of individuals a “sensor group.”
By finding and monitoring the tweets of a sensor group, we can catch—and sometimes even predict—the outbreak of contagious information early on. That detection edge could improve how we track the outbreak of disease epidemics, the rise of certain terms or phrases, or shifts in political sentiment.
Whereas Flu Trends relies on a relatively static, proprietary “dictionary” of flu-related search terms based on average Google search habits, the sensor method taps into what is really happening in social networks in real time.
By drawing on language being used by a sensor group—such as mentions of an emergent symptom or a popular newly coined name for a disease—Google could gain insight into what their dictionary might be missing.Sampling both the average Googler’s behavior and that of the exceptionally connected social network user can paint a much fuller picture of whatever landscape we are interested in tracking. We can more accurately see how it looks now—and how it could look in the near future.