# projectFlock

a twitter botnet with bots replying tweets with text generated by Markov chains

##### 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 (http://www.gutenberg.org/cache/epub/4280/pg4280.txt).

#### Replying tweets with Markov chains
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.

In the following examples, the bots ("andreimarkov", "dodecahedron", "projectNSA") are replying some people.

![Argos](https://raw.githubusercontent.com/arnaucode/projectFlock/master/screenshots/01.png "01")

![Argos](https://raw.githubusercontent.com/arnaucode/projectFlock/master/screenshots/02.jpeg "02")

![Argos](https://raw.githubusercontent.com/arnaucode/projectFlock/master/screenshots/03.jpeg "03")

![Argos](https://raw.githubusercontent.com/arnaucode/projectFlock/master/screenshots/04.jpeg "04")


configuration file example (flockConfig.json):
```
[{
        "title": "account1",
        "consumer_key": "xxxxxxxxxxxxx",
        "consumer_secret": "xxxxxxxxxxxxx",
        "access_token_key": "xxxxxxxxxxxxx",
        "access_token_secret": "xxxxxxxxxxxxx"
    },
    {
        "title": "account2",
        "consumer_key": "xxxxxxxxxxxxx",
        "consumer_secret": "xxxxxxxxxxxxx",
        "access_token_key": "xxxxxxxxxxxxx",
        "access_token_secret": "xxxxxxxxxxxxx"
    },
    {
        "title": "account3",
        "consumer_key": "xxxxxxxxxxxxx",
        "consumer_secret": "xxxxxxxxxxxxx",
        "access_token_key": "xxxxxxxxxxxxx",
        "access_token_secret": "xxxxxxxxxxxxx"
    }
]

```
