class: center, middle, inverse, title-slide .title[ # The Parable of Google Flu Trends ] .subtitle[ ## A Social Data Science Story ] .author[ ### David Garcia <br><br> <em>ETH Zurich, Chair of Systems Design</em> ] .date[ ### Social Data Science ] --- layout: true <div class="my-footer"><span>David Garcia - Social Data Science - ETH Zurich, Chair of Systems Design</span></div> --- # Gooogle Trends Google Trends is a website hosted by Google that allows you to get a measurement of Google search volume for a term <center> <img src="EU.png" alt="" width="900"/> </center> --- ## Nowcasting flu incidence with Google Trends <div style="float:right"> <img src="GoogleFluTrends.jpg" alt="Data Science discipline Venn diagram" width="500px"/> </div> **Nowcasting** is predicting the present. It provides an estimation of the value of a quantity based on signals that appear at the same time. Google Flu Trends aimed at nowcasting influenza-related physician visits based on Google search volumes. As reported in the [Nature paper](http://www.nature.com/nature/journal/v457/n7232/full/nature07634.html), Google Flu Trends achieved a high weekly accuracy between 2003 and 2008. The figure shows the nowcasting result of Google flu trends. CDC data is published with a delay of two weeks. --- ## When Google Flu Trends Stopped working <center> <img src="FluFail.jpg" alt="drawing" width="750"/> </center> --- ## When Google Flu Trends Stopped working In 2013, Google Flu Trends [started overestimating](https://www.nature.com/news/when-google-got-flu-wrong-1.12413) the incidence of flu cases. A [follow-up study in Science](http://science.sciencemag.org/content/343/6176/1203 ) showed that just a lagged CDC prediction with 2 week-old data outperformed Google Flu Trends, without using any Google data. Since many details about how Google and Google Trends work are not public, we cannot know for sure what was the surce of the error. Some candidates are news-related searches, seasonal effects, or changes in Google's algorithm and interface. Google Flu Trends is an example of **Big Data Hubris**: ”The often implicit assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis” [(Lazer et al, Science, 2014)](http://science.sciencemag.org/content/343/6176/1203) > Take home message: **All data is better than Big Data** --- ## Beware of Making a Data Piñata ](Pinata.png) Also known as kitchen-sink regression, garbage-in garbage-out, and post hoc storytelling. Some patterns will always come out with sufficient data, what matters is making sense of them.