More detailed explanation
Generating text with Markov chains
The algorithm calculates the probabilities of Markov chains, analyzing a considerable amount of text, for the examples, I've done it with the book "The Critique of Pure Reason", by Immanuel Kant.
Each word is considered as a Markov state, and the algorithm calculates the probabilities to get the next state. After processing a large amount of text, it have a 'memory' with all the possible states of the text with the probabilities to the next states.
Replying tweets about topic
When the botnet is up working, the bots start streaming all the twitter new tweets containing the configured keywords. Each bot takes a tweet, analyzes the containing words, and generates a reply using the Markov chains previously calculated, and posts the tweet as reply.
Result
The result is a twitter botnet, controlled by the script. The botnet interact with humans, replying responses generated with the algorithm.
In the examples can see the type of response. For future versions, can be a good point to feed the text database with more books, to get a text database bigger.
