In Reply Finding imperfections in big data is like “harpooning a blimp—it’s impossible to miss, and every thrust is likely to be fatal.”1 Dr Benke lists reasons why social media may not perfectly represent, for example, the true burden of a disease. There are several reasons to choose from, as the Sommer Editorial2 indicates. Existing streams of data, including Google searches and Tweets used in our report, are neither population-based nor unbiased.3 Even more troubling are the instances in which activity would pass current deep-learning filters but have no relation to the true outcome of interest. Interest in “pink eye” could have risen because of a 2008 movie of that name. No one is saying that increased activity surrounding “smallpox” in January 2005 suggested a true underlying epidemic of the disease.4
Deiner MS, Lietman TM, Porco TC. Uncertainties in Big Data When Using Internet Surveillance Tools and Social Media for Determining Patterns in Disease Incidence—Reply. JAMA Ophthalmol. 2017;135(4):402–403. doi:10.1001/jamaophthalmol.2017.0140
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