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 A s people increasingly turn to the Internet for news, information, and research purposes, it is tempting to view online activity at any moment in time as a snapshot of the collective consciousness, reflecting the instantaneous interests, concerns, and intentions of the global population (1, 2). From this perspective, it is a short step to conclude that what people are searching for today is predictive of what they will do in the near future. Consumers contemplating buying a new camera may search to compare models; moviegoers may search to determine the opening date of a new film, or to locate cinemas showing it; and individuals planning a vacation may search for places of interest, to find airline tickets, or to price hotel rooms. If so, it follows that by appropriately aggregating counts of search queries related to retail activity, moviegoing, or travel, one might be able to predict collective behavior of economic, cultural, or political interest. Determining the nature of behavior that can be predicted using search, the accuracy of such predictions, and the time scale over which predictions can be usefully made are therefore all questions of interest. Although previous work has considered the relation between search volume and offline outcomes, researchers have focused on the observation that search "predicts the present" (3, 4), meaning that search volume correlates with contemporaneous events. For example, Ettredge et al. (5) found that counts of the top 300 search terms during 2001-2003 were correlated with US Bureau of Labor Statistics unemployment figures; Cooper et al. (6) found that search activity for specific cancers during 2001-2003 correlated with their estimated incidence; and Eysenbach (7) found a high correlation between clicks on sponsored search results for flu-related keywords and epidemiological data from the 2004-2005 Canadian flu season. More recently, Polgreen et al. ( 8) showed that search volume for handpicked influenza-related queries was correlated with subsequently reported caseloads over the period 2004-2008, and Hulth et al. (9) found similar results in a study of search queries submitted on a Swedish medical Web site. An automated procedure for identifying informative queries is described in Ginsberg et al. (10), and based on that methodol-ogy, Google Flu Trends (http://www.google.org/flutrends) provides real-time estimates of flu incidence in several countries. Finally, Choi and Varian (3, 4) have compared search volume to economic activity, including auto and home sales, international visitor statistics, and US unemployment claims; and similar work has been reported for German unemployment claims (11). In this paper, we further this work by considering the ability of search to predict events days or weeks in advance of their actual occurrence. In so doing we also emphasize an often overlooked aspect of prediction-namely, that performance is relative. To illustrate, consider predicting the weather in Santa Fe, New Mexico, where it is sunny 300 days a year. A prediction of sunshine every day would be correct 82% of the time, yet hardly impressive; nor could a model that fails to outperform the simple, autoregressive rule that tomorrow's outcome will be like today's be said to be predictive in any interesting way. Correspondingly, the predictive power of search should be judged in relation to statistical models fit with traditional data sources, prediction markets, or expert opinions-a point that, with some exceptions (3, 4), has been overlooked in previous related work. Finally, we extend the domain of past studies from epidemiological and macroeconomic time series to include consumer activity such as that associated with movies, music, and video games. These are a natural class of events to consider as they represent activities (e.g., attending a movie) for which it is plausible that individuals might (i) harbor the intention to perform the corresponding action sometime in advance of actually fulfilling it and (ii) signal that intention through a related Web search. In this sense, our paper is related to other work that uses online chatter and Twitter posts to predict rankings of books on Amazon.com (1) and movie box-office revenues (2), respectively. However, by studying three classes of outcomes-the opening weekend box-office ticket sales for feature films, the first-month revenues of video games, and the weekly ranks of songs on the Billboard Hot 100 chart-we reveal some additional insights regarding variation across domains in how search-based predictions perform, both in an absolute sense and relative to alternative forecasting methods. The potential predictive power of search activity is illustrated in Fig. 1 , which shows the volume of people searching for the movie Transformers 2 (Fig. 1A ) and the video game Tom Clancy's H.A.W.X. (Fig. 1B) around their release dates. In both cases searches peak either on or slightly after the release date, but precursors to the spikes appear several days or even weeks in advance. Correspondingly for music, Fig. 1C shows that the rank of the song "Right Round" in terms of search volume closely tracks its rank on the Billboard Hot 100 chart.
Goel et al. REF show that what users are searching for online can also predict their collective future behavior days or even weeks in advance.
Predicting consumer behavior with Web search
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Computer Science Engineering Medicine