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73
74 <h1>Auto generated tweets from Markov chains</h1>
75
76<p><em>2017-12-29</em></p>
77
78<p>The main goal of this article is to explain how to develop a twitter botnet with autonomous bots replying tweets with text generated based on probabilities in Markov chains. As this is a project to learn, we have made everything from scratch.</p>
79
80<p>The idea of mixing twitter bots with Markov chains was in a Twitter conversation with <a href="https://twitter.com/x0rz">@x0rz</a></p>
81
82<h2>1. Markov chains</h2>
83
84<p>A Markov chain is a sequence of stochastic events (based on probabilities) where the current state of a variable or system is independent of all past states, except the current state.</p>
85
86<p><a href="https://en.wikipedia.org/wiki/Markov_chain">https://en.wikipedia.org/wiki/Markov_chain</a></p>
87
88<p>In our case, we will use Markov chains to analyze the probabilities that after a word comes another concrete word. So, we will generate some diagram like the following one, but with thousands of words.</p>
89
90<p><img src="img/posts/flock-botnet/markovchain.png" alt="flock-botnet" title="Markov chain" /></p>
91
92<p>In our case, we need as input some text document with thousands of words, to get a better input data. In this example we have made it with the book “The Critique of Pure Reason”, by Immanuel Kant (<a href="http://www.gutenberg.org/cache/epub/4280/pg4280.txt">http://www.gutenberg.org/cache/epub/4280/pg4280.txt</a>), just because is the first book that we have found in .txt format.</p>
93
94<h3>1.1 Calculating the Markov chains</h3>
95
96<p>First we need to read the text file:</p>
97
98<pre><code class="language-go">func readTxt(path string) (string, error) {
99 data, err := ioutil.ReadFile(path)
100 if err != nil {
101 //Do something
102 }
103 dataClean := strings.Replace(string(data), "\n", " ", -1)
104 content := string(dataClean)
105 return content, err
106}
107</code></pre>
108
109<p>To calculate the probabilities of the Markov states, we have made the following function that analyzes the full input text, and stores the Markov states:</p>
110
111<pre><code class="language-go">func calcMarkovStates(words []string) []State {
112 var states []State
113 //count words
114 for i := 0; i < len(words)-1; i++ {
115 var iState int
116 states, iState = addWordToStates(states, words[i])
117 if iState < len(words) {
118 states[iState].NextStates, _ = addWordToStates(states[iState].NextStates, words[i+1])
119 }
120
121 printLoading(i, len(words))
122 }
123
124 //count prob
125 for i := 0; i < len(states); i++ {
126 states[i].Prob = (float64(states[i].Count) / float64(len(words)) * 100)
127 for j := 0; j < len(states[i].NextStates); j++ {
128 states[i].NextStates[j].Prob = (float64(states[i].NextStates[j].Count) / float64(len(words)) * 100)
129 }
130 }
131 fmt.Println("\ntotal words computed: " + strconv.Itoa(len(words)))
132 return states
133}
134</code></pre>
135
136<p>The <em>printLoading</em> function, is just a simple function to print in the terminal the % of the process done:</p>
137
138<pre><code class="language-go">func printLoading(n int, total int) {
139 var bar []string
140 tantPerFourty := int((float64(n) / float64(total)) * 40)
141 tantPerCent := int((float64(n) / float64(total)) * 100)
142 for i := 0; i < tantPerFourty; i++ {
143 bar = append(bar, "█")
144 }
145 progressBar := strings.Join(bar, "")
146 fmt.Printf("\r " + progressBar + " - " + strconv.Itoa(tantPerCent) + "")
147}
148</code></pre>
149
150<p><img src="img/posts/flock-botnet/progressbarMarkov.gif" alt="flock-botnet" title="Markov chain" /></p>
151
152<h3>1.2 Generating text from the Markov chains</h3>
153
154<p>To generate the text, we will need a initializer word and the length of the output text to generate. Then, we perform a loop and get words based on the Markov chains probabilities calculated in the previous step.</p>
155
156<pre><code class="language-go">func (markov Markov) generateText(states []State, initWord string, count int) string {
157 var generatedText []string
158 word := initWord
159 generatedText = append(generatedText, word)
160 for i := 0; i < count; i++ {
161 word = getNextMarkovState(states, word)
162 if word == "word no exist on the memory" {
163 return "word no exist on the memory"
164 }
165 generatedText = append(generatedText, word)
166 }
167 text := strings.Join(generatedText, " ")
168 return text
169}
170</code></pre>
171
172<p>To generate the text we need a function that given the markov chains and a word, returns a random probability based word to be the next word following the given word:</p>
173
174<pre><code class="language-go">func getNextMarkovState(states []State, word string) string {
175 iState := -1
176 for i := 0; i < len(states); i++ {
177 if states[i].Word == word {
178 iState = i
179 }
180 }
181 if iState < 0 {
182 return "word no exist on the memory"
183 }
184 var next State
185 next = states[iState].NextStates[0]
186 next.Prob = rand.Float64() * states[iState].Prob
187 for i := 0; i < len(states[iState].NextStates); i++ {
188 if (rand.Float64()*states[iState].NextStates[i].Prob) > next.Prob && states[iState-1].Word != states[iState].NextStates[i].Word {
189 next = states[iState].NextStates[i]
190 }
191 }
192 return next.Word
193}
194</code></pre>
195
196<h2>2. Twitter API</h2>
197
198<p>To interact with the Twitter API, we will use <strong>go-twitter</strong> library <a href="https://github.com/dghubble/go-twitter">https://github.com/dghubble/go-twitter</a> .</p>
199
200<p>We setup a streaming connection with the Twitter API, we will filter tweets by some words related to our input dataset:</p>
201
202<pre><code class="language-go">func startStreaming(states []State, flock Flock, flockUser *twitter.Client, botScreenName string, keywords []string) {
203 // Convenience Demux demultiplexed stream messages
204 demux := twitter.NewSwitchDemux()
205 demux.Tweet = func(tweet *twitter.Tweet) {
206 if isRT(tweet) == false && isFromBot(flock, tweet) == false {
207 processTweet(states, flockUser, botScreenName, keywords, tweet)
208 }
209 }
210 demux.DM = func(dm *twitter.DirectMessage) {
211 fmt.Println(dm.SenderID)
212 }
213 demux.Event = func(event *twitter.Event) {
214 fmt.Printf("%#v\n", event)
215 }
216
217 fmt.Println("Starting Stream...")
218 // FILTER
219 filterParams := &twitter.StreamFilterParams{
220 Track: keywords,
221 StallWarnings: twitter.Bool(true),
222 }
223 stream, err := flockUser.Streams.Filter(filterParams)
224 if err != nil {
225 log.Fatal(err)
226 }
227 // Receive messages until stopped or stream quits
228 demux.HandleChan(stream.Messages)
229}
230</code></pre>
231
232<p>Then, each time that a new tweet with some of our tracking words is tweeted, we process that tweet and then we generate a reply based on the Markov chains, and we post that reply:</p>
233
234<pre><code class="language-go">func processTweet(states []State, flockUser *twitter.Client, botScreenName string, keywords []string, tweet *twitter.Tweet) {
235 c.Yellow("bot @" + botScreenName + " - New tweet detected:")
236 fmt.Println(tweet.Text)
237
238 tweetWords := strings.Split(tweet.Text, " ")
239 generatedText := "word no exist on the memory"
240 for i := 0; i < len(tweetWords) && generatedText == "word no exist on the memory"; i++ {
241 fmt.Println(strconv.Itoa(i) + " - " + tweetWords[i])
242 generatedText = generateMarkovResponse(states, tweetWords[i])
243 }
244 c.Yellow("bot @" + botScreenName + " posting response")
245 fmt.Println(tweet.ID)
246 replyTweet(flockUser, "@"+tweet.User.ScreenName+" "+generatedText, tweet.ID)
247 waitTime(1)
248}
249</code></pre>
250
251<pre><code class="language-go">func replyTweet(client *twitter.Client, text string, inReplyToStatusID int64) {
252 tweet, httpResp, err := client.Statuses.Update(text, &twitter.StatusUpdateParams{
253 InReplyToStatusID: inReplyToStatusID,
254 })
255 if err != nil {
256 fmt.Println(err)
257 }
258 if httpResp.Status != "200 OK" {
259 c.Red("error: " + httpResp.Status)
260 c.Purple("maybe twitter has blocked the account, CTRL+C, wait 15 minutes and try again")
261 }
262 fmt.Print("tweet posted: ")
263 c.Green(tweet.Text)
264}
265</code></pre>
266
267<h2>3. Flock-Botnet, or how to avoid the Twitter API limitations</h2>
268
269<p>If you ever played with the Twitter API, you will have seen that there are some restrictions and limitations. That means that if your bot have too much posting activity, the account will get blocked for some minutes.</p>
270
271<p>To avoid this limitation, we will deploy a botnet, where each bot will be replying tweets based on the Markov chains probabilities. In this way, when a bot post a tweet reply, the bot falls asleep for 1 minute. In the meantime, the other bots will be processing and replying the other tweets.</p>
272
273<p><img src="img/posts/flock-botnet/flock-botnet-scheme.png" alt="flock-botnet" title="01" /></p>
274
275<h2>3. Putting it all together</h2>
276
277<p>In this demo, we will use only 3 bots (twitter accounts).</p>
278
279<p>The botnet configuration will be in the config.json file:</p>
280
281<pre><code class="language-json">[{
282 "title": "bot1",
283 "consumer_key": "xxxxxxxxxxxxx",
284 "consumer_secret": "xxxxxxxxxxxxx",
285 "access_token_key": "xxxxxxxxxxxxx",
286 "access_token_secret": "xxxxxxxxxxxxx"
287 },
288 {
289 "title": "bot2",
290 "consumer_key": "xxxxxxxxxxxxx",
291 "consumer_secret": "xxxxxxxxxxxxx",
292 "access_token_key": "xxxxxxxxxxxxx",
293 "access_token_secret": "xxxxxxxxxxxxx"
294 },
295 {
296 "title": "bot3",
297 "consumer_key": "xxxxxxxxxxxxx",
298 "consumer_secret": "xxxxxxxxxxxxx",
299 "access_token_key": "xxxxxxxxxxxxx",
300 "access_token_secret": "xxxxxxxxxxxxx"
301 }
302]
303</code></pre>
304
305<p>The complete process will be:</p>
306
307<p><img src="img/posts/flock-botnet/steps.png" alt="flock-botnet" title="01" /></p>
308
309<h2>4. Demo</h2>
310
311<p>We have setted up a small demo with 3 bots. As we have said in the beging of this post, we have used the <a href="http://www.gutenberg.org/cache/epub/4280/pg4280.txt">“The Critique of Pure Reason”, by Immanuel Kant </a> to generate the Markov chains.</p>
312
313<p>When the botnet is up working, the bots start streaming all the twitter new tweets containing the configured keywords.</p>
314
315<p>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.</p>
316
317<p>Example of terminal view during the flock-botnet execution:</p>
318
319<p><img src="img/posts/flock-botnet/terminal00.png" alt="flock-botnet" title="01" /></p>
320
321<p>Here is an example of the execution:</p>
322
323<p><img src="img/posts/flock-botnet/flock-botnet-demo.gif" alt="flock-botnet" title="Markov chain" /></p>
324
325<p>In the following examples, there are some screenshots that the bots (“@andreimarkov”, “@dodecahedron”, “@projectNSA”) have been replying to some people.</p>
326
327<p><img src="img/posts/flock-botnet/01.png" alt="flock-botnet" title="01" /></p>
328
329<hr>
330
331<p><img src="img/posts/flock-botnet/02.jpeg" alt="flock-botnet" title="02" /></p>
332
333<hr>
334
335<p><img src="img/posts/flock-botnet/03.jpeg" alt="flock-botnet" title="03" /></p>
336
337<hr>
338
339<p><img src="img/posts/flock-botnet/04.jpeg" alt="flock-botnet" title="04" /></p>
340
341<h2>Conclusion</h2>
342
343<p>In this article, we have seen how to build a Twitter botnet with the bots replying tweets with text generated based on Markov chains.</p>
344
345<p>As in this article we have used only 1 grade Markov chains, so the generated text is not really like humans text. But for future projects, a good choice would be combining more grades Markov chains with other text mining techniques.</p>
346
347<p>Twitter API have lots of uses, and in this post we have seen one of them. I hope to be able to write some more articles about other projects arround the Twitter API. For example some Twitter <a href="https://devpost.com/software/projectnsa">network nodes analysis</a>, or some <a href="https://arnaucube.com/hashtagsUsersNetworkPage.html">users & hashtags analysis</a>.</p>
348
349<p>The complete code of this project is able in <a href="https://github.com/arnaucube/flock-botnet">https://github.com/arnaucube/flock-botnet</a></p>
350
351<p>Project page: <a href="http://arnaucube.com/flock-botnet/">http://arnaucube.com/flock-botnet/</a></p>
352
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