Question: Markov chains are a (almost standard) way to generate [random gibberish](http://uswaretech.com/blog/2009/06/pseudo-random-text-markov-chains-python/) which looks intelligent to untrained eye. How would you go about identifying markov generated text from human written text.

It would be awesome if the resources you point to are Python friendly.

Answer: 
If you had several large Markov-generated texts, you could possibly determine that they were so by comparing the word frequencies between each of the samples. Since Markov chains depend on constant word probabilities, the proportions of any given word should be roughly equal from sample to sample.
If you write a program which generates Markovian transition probabilities from any sequence of symbols, and then calculates the entropy rate of the markov matrix. (see <http://en.wikipedia.org/wiki/Entropy_rate#Entropy_rates_for_Markov_chains>) This is basically an estimate of how easily the text could be predicted using just the markov chain (higher entropy means *harder* to predict). Therefore I would think that the lower the entropy of the markov matrix is, the more likely that the sample of text is controlled by a markov matrix. If you have questions on how to write this code, I happen to have a program in python which does exactly this on my computer, so I can help you out