The Parable of Google Flu: Traps in Big Data Analysis

Journal Article
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David Lazer
Ryan Patrick Kennedy
Gary King
Alessandro Vespignani
The Parable of Google Flu: Traps in Big Data Analysis

In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States (1, 2). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data (3, 4), what lessons can we draw from this error?

The problems we identify are not limited to GFT. Research on whether search or social media can predict x has become commonplace (5–7) and is often put in sharp contrast with traditional methods and hypotheses. Although these studies have shown the value of these data, we are far from a place where they can supplant more traditional methods or theories (8). We explore two issues that contributed to GFT’s mistakes, big data hubris and algorithm dynamics, and offer lessons for moving forward in the big data age.

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