Google Correlate Whitepaper
Venue
Google (2011)
Publication Year
2011
Authors
Matt Mohebbi, Dan Vanderkam, Julia Kodysh, Rob Schonberger, Hyunyoung Choi, Sanjiv Kumar
BibTeX
Abstract
Trends in online web search query data have been shown useful in providing models
of real world phenomena. However, many of these results rely on the careful choice
of queries that prior knowledge suggests should correspond with the phenomenon.
Here, we present an online, automated method for query selection that does not
require such prior knowledge. Instead, given a temporal or spatial pattern of
interest, we determine which queries best mimic the data. These search queries can
then serve to build an estimate of the true value of the phenomenon. We present the
application of this method to produce accurate models of influenza activity and
home refinance rate in the United States. We additionally show that spatial
patterns in real world activity and temporal patterns in web search query activity
can both surface interesting and useful correlations.
