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On April 23, 2013 the stock market experienced one of its biggest flash-crash drops of the year, with the Dow Jones industrial average falling 143 points (over 1%) in a matter of minutes. Unlike the 2012 stock market blip, this one wasn't caused by an individual trade, but rather by a single tweet from AP's account on the social network, Twitter. The tweet, of course, wasn't written by AP, but rather by an imposter who had temporarily gained control of the account. Considering the impact of real-time messaging services, such as Twitter, what if it were possible to detect the tweet as hacked? In this presentation, we'll discuss how to use machine learning and "big data" analysis to mine large amounts of information and classify meaningful relationships from them. In particular, we'll walk-through a prototype machine learning example that attempts to classify tweets as having been authored by AP or not. We'll examine learning curves to see how they help validate machine learning algorithms and models. As a final test, we'll run the program on the hacked tweet and see if it's able to successfully classify the tweet as being authentic or hacked.