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# projectFlock |
# projectFlock |
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a twitter botnet with bots replying tweets with text generated by Markov chains |
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##### generating text with Markov chains |
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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 (http://www.gutenberg.org/cache/epub/4280/pg4280.txt). |
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#### Replying tweets with Markov chains |
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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. |
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In the following examples, the bots ("andreimarkov", "dodecahedron", "projectNSA") are replying some people. |
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![Argos](https://raw.githubusercontent.com/arnaucode/projectFlock/master/screenshots/01.png "01") |
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![Argos](https://raw.githubusercontent.com/arnaucode/projectFlock/master/screenshots/02.jpeg "02") |
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![Argos](https://raw.githubusercontent.com/arnaucode/projectFlock/master/screenshots/03.jpeg "03") |
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![Argos](https://raw.githubusercontent.com/arnaucode/projectFlock/master/screenshots/04.jpeg "04") |
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configuration file example (flockConfig.json): |
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``` |
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[{ |
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"title": "account1", |
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"consumer_key": "xxxxxxxxxxxxx", |
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"consumer_secret": "xxxxxxxxxxxxx", |
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"access_token_key": "xxxxxxxxxxxxx", |
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"access_token_secret": "xxxxxxxxxxxxx" |
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}, |
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{ |
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"title": "account2", |
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"consumer_key": "xxxxxxxxxxxxx", |
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"consumer_secret": "xxxxxxxxxxxxx", |
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"access_token_key": "xxxxxxxxxxxxx", |
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"access_token_secret": "xxxxxxxxxxxxx" |
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}, |
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{ |
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"title": "account3", |
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"consumer_key": "xxxxxxxxxxxxx", |
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"consumer_secret": "xxxxxxxxxxxxx", |
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"access_token_key": "xxxxxxxxxxxxx", |
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"access_token_secret": "xxxxxxxxxxxxx" |
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} |
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] |
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``` |