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Research Article

Predicting consumer behavior with Web search

Sharad Goel, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, and Duncan J. Watts
PNAS October 12, 2010 107 (41) 17486-17490; https://doi.org/10.1073/pnas.1005962107
Sharad Goel
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  • For correspondence: goel@yahoo-inc.com hofman@yahoo-inc.com lahaies@yahoo-inc.com pennockd@yahoo-inc.com djw@yahoo-inc.com
Jake M. Hofman
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Sébastien Lahaie
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David M. Pennock
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Duncan J. Watts
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  • For correspondence: goel@yahoo-inc.com hofman@yahoo-inc.com lahaies@yahoo-inc.com pennockd@yahoo-inc.com djw@yahoo-inc.com
  1. Edited* by Simon A. Levin, Princeton University, Princeton, NJ, and approved August 10, 2010 (received for review April 29, 2010)

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Abstract

Recent work has demonstrated that Web search volume can “predict the present,” meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.

  • culture
  • predictions

Footnotes

  • 1To whom correspondence may be addressed. E-mail: goel{at}yahoo-inc.com, hofman{at}yahoo-inc.com, lahaies{at}yahoo-inc.com, pennockd{at}yahoo-inc.com, or djw{at}yahoo-inc.com.
  • Author contributions: S.G., J.M.H., S.L., D.M.P., and D.J.W. designed research; S.G., J.M.H., and S.L. performed research; S.G., J.M.H., and S.L. analyzed data; and S.G., J.M.H., S.L., D.M.P., and D.J.W. wrote the paper.

  • The authors declare no conflict of interest.

  • *This Direct Submission article had a prearranged editor.

Freely available online through the PNAS open access option.

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Predicting consumer behavior with Web search
Sharad Goel, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, Duncan J. Watts
Proceedings of the National Academy of Sciences Oct 2010, 107 (41) 17486-17490; DOI: 10.1073/pnas.1005962107

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Predicting consumer behavior with Web search
Sharad Goel, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, Duncan J. Watts
Proceedings of the National Academy of Sciences Oct 2010, 107 (41) 17486-17490; DOI: 10.1073/pnas.1005962107
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