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Google Correlate Whitepaper

Matt Mohebbi
Dan Vanderkam
Julia Kodysh
Rob Schonberger
Hyunyoung Choi
Google (2011)


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.