The document details a workshop on data analysis using Python and PySpark, focusing on classifying bot behavior in Twitter data. It outlines an experimental design involving data ingestion, preprocessing, classification with various models, and evaluation metrics. Important results include the identification of bot patterns and the effectiveness of machine learning algorithms in distinguishing between human and bot accounts.

1. Bot Not?
@erinshellman
PyData Seattle, July 26, 2015
orEnd-to-end data analysis in Python

3. PySpark Workshop
@Tune
August 27,6-8pm
Starting a new career in software
@Moz
October 22,6-8pm

4. Q: Why?
Bots are fun.
Q: How?
Python.

7. In 2009,24% of tweets
were generated by bots.

8. Last year Twitter disclosed
that 23 million of its active
users were bots.

11. Hypothesis:
Bot behavior is
differentiable from
human behavior.

12. ExperimentalDesign
• Ingest data
• Clean and process data
• Create a classifier

13. ExperimentalDesign
• Ingest data
• python-twitter
• Clean and process data
• Pandas,NLTK,Seaborn,iPython Notebooks
• Create a classifier
• Scikit-learn

14. Step1:
Getdata.

16. lollollollol

20. ConnectingtoTwitter
https://github.com/bear/python-twitter

21. def get_friends(self, screen_name, count = 5000):
'''
GET friends/ids i.e. people you follow
returns a list of JSON blobs
'''
friends = self.api.GetFriendIDs(screen_name = screen_name,
count = count)
return friends

23. # break query into bite-size chunks 🍔
def blow_chunks(self, data, max_chunk_size):
for i in range(0, len(data), max_chunk_size):
yield data[i:i + max_chunk_size]

24. if len(user_ids) > max_query_size:
chunks = self.blow_chunks(user_ids, max_chunk_size = max_query_size)
while True:
try:
current_chunk = chunks.next()
for user in current_chunk:
try:
user_data = self.api.GetUser(user_id = str(user))
results.append(user_data.AsDict())
except:
print "got a twitter error! D:"
pass
print "nap time. ZzZzZzzzzz..."
time.sleep(60 * 16)
continue
except StopIteration:
break

25. if len(user_ids) > max_query_size:
chunks = self.blow_chunks(user_ids, max_chunk_size = max_query_size)
while True:
try:
current_chunk = chunks.next()
for user in current_chunk:
try:
user_data = self.api.GetUser(user_id = str(user))
results.append(user_data.AsDict())
except:
print "got a twitter error! D:"
pass
print "nap time. ZzZzZzzzzz..."
time.sleep(60 * 16)
continue
except StopIteration:
break

26. if len(user_ids) > max_query_size:
chunks = self.blow_chunks(user_ids, max_chunk_size = max_query_size)
while True:
try:
current_chunk = chunks.next()
for user in current_chunk:
try:
user_data = self.api.GetUser(user_id = str(user))
results.append(user_data.AsDict())
except:
print "got a twitter error! D:"
pass
print "nap time. ZzZzZzzzzz..."
time.sleep(60 * 16)
continue
except StopIteration:
break

27. if len(user_ids) > max_query_size:
chunks = self.blow_chunks(user_ids, max_chunk_size = max_query_size)
while True:
try:
current_chunk = chunks.next()
for user in current_chunk:
try:
user_data = self.api.GetUser(user_id = str(user))
results.append(user_data.AsDict())
except:
print "got a twitter error! D:"
pass
print "nap time. ZzZzZzzzzz..."
time.sleep(60 * 16)
continue
except StopIteration:
break

28. {
"name": "Twitter API",
"location": "San Francisco, CA",
"created_at": "Wed May 23 06:01:13 +0000 2007",
"default_profile": true,
"favourites_count": 24,
"url": "http://dev.twitter.com",
"id": 6253282,
"profile_use_background_image": true,
"listed_count": 10713,
"profile_text_color": "333333",
"lang": "en",
"followers_count": 1198334,
"protected": false,
"geo_enabled": true,
"description": "The Real Twitter API.”,
"verified": true,
"notifications": false,
"time_zone": "Pacific Time (US & Canada)",
"statuses_count": 3331,
"status": {
"coordinates": null,
"created_at": "Fri Aug 24 16:15:49 +0000 2012",
"favorited": false,
"truncated": false,

29. sample size = 8509 accounts

30. Step2:
preprocessing.

31. Who'sready
1. “Flatten” the JSON into one
row per user.
2.Variable recodes. e.g.
consistently denoting
missing values, True/False
into 1/0
3.Select only desired features
for modeling.
toclean?

35. Howto
makedata
withthis?

37. e.g.LexicalDiversity
• A token is a sequence of characters that we want
to treat as a group.
• For instance, lol, #blessed, or 💉🔪💇
• Lexicaldiversity is the ratio of unique tokens to
total tokens.

38. def lexical_diversity(text):
if len(text) == 0:
diversity = 0
else:
diversity = float(len(set(text))) / len(text)
return diversity

39. # Easily compute summaries for each user!
grouped = tweets.groupby('screen_name')
diversity = grouped.apply(lexical_diversity)

41. Step3:
Classification.

43. # Naive Bayes
bayes = GaussianNB().fit(train[features], y)
bayes_predict = bayes.predict(test[features])
# Logistic regression
logistic = LogisticRegression().fit(train[features], y)
logistic_predict = logistic.predict(test[features])
# Random Forest
rf = RandomForestClassifier().fit(train[features], y)
rf_predict = rf.predict(test[features])
# Classification Metrics
print(metrics.classification_report(test.bot, bayes_predict))
print(metrics.classification_report(test.bot, logistic_predict))
print(metrics.classification_report(test.bot, rf_predict))

44. precision recall f1-score
0.0 0.97 0.27 0.42
1.0 0.20 0.95 0.33
avg / total 0.84 0.38 0.41
precision recall f1-score
0.0 0.85 1.00 0.92
1.0 0.94 0.14 0.12
avg / total 0.87 0.85 0.79
precision recall f1-score
0.0 0.91 0.98 0.95
1.0 0.86 0.51 0.64
avg / total 0.90 0.91 0.90
Naive Bayes
Logistic Regression
Random Forest

45. # construct parameter grid
param_grid = {'max_depth': [1, 3, 6, 9, 12, 15, None],
'max_features': [1, 3, 6, 9, 12],
'min_samples_split': [1, 3, 6, 9, 12, 15],
'min_samples_leaf': [1, 3, 6, 9, 12, 15],
'bootstrap': [True, False],
'criterion': ['gini', 'entropy']}
# fit best classifier
grid_search = GridSearchCV(RandomForestClassifier(), param_grid = param_grid).fit(train[features], y)
# assess predictive accuracy
predict = grid_search.predict(test[features])
print(metrics.classification_report(test.bot, predict))

46. print(grid_search.best_params_)
{'bootstrap': True,
'min_samples_leaf': 15,
'min_samples_split': 9,
'criterion': 'entropy',
'max_features': 6,
'max_depth': 9}
Best parameter set
for random forest

47. precision recall f1-score
0.0 0.93 0.99 0.96
1.0 0.89 0.59 0.71
avg / total 0.92 0.93 0.92
precision recall f1-score
0.0 0.91 0.98 0.95
1.0 0.86 0.51 0.64
avg / total 0.90 0.91 0.90
Default Random Forest
Tuned Random Forest

49. Iterative model development
in Scikit-learn is laborious.

50. logistic_model = train(bot ~ statuses_count + friends_count + followers_count,
data = train,
method = 'glm',
family = binomial,
preProcess = c('center', 'scale'))

51. > confusionMatrix(logistic_predictions, test$bot)
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 394 22
1 144 70
Accuracy : 0.7365
95% CI : (0.7003, 0.7705)
No Information Rate : 0.854
P-Value [Acc > NIR] : 1
Kappa : 0.3183
Mcnemars Test P-Value : <2e-16
Sensitivity : 0.7323
Specificity : 0.7609
Pos Pred Value : 0.9471
Neg Pred Value : 0.3271
Prevalence : 0.8540
Detection Rate : 0.6254
Detection Prevalence : 0.6603
Balanced Accuracy : 0.7466
'Positive' Class : 0

52. > summary(logistic_model)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-1.2620 -0.6323 -0.4834 -0.0610 6.0228
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.7136 0.7293 -7.835 4.71e-15 ***
statuses_count -2.4120 0.5026 -4.799 1.59e-06 ***
friends_count 30.8238 3.2536 9.474 < 2e-16 ***
followers_count -69.4496 10.7190 -6.479 9.22e-11 ***
---
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2172.3 on 2521 degrees of freedom
Residual deviance: 1858.3 on 2518 degrees of freedom
AIC: 1866.3
Number of Fisher Scoring iterations: 13

53. # compare models
results = resamples(list(tree_model = tree_model,
bagged_model = bagged_model,
boost_model = boost_model))
# plot results
dotplot(results)

54. Step5:
Pontificate.

55. Pythonrules!
• The Python language is an incredibly powerful tool for end-
to-end data analysis.
• Even so,some tasks are more work than they should be.

56. Lamebots

57. Andnow…

58. thebots.

64. Clicks
• Twitter Is Plagued With 23 Million Automated Accounts: http://valleywag.gawker.com/twitter-is-riddled-with-23-million-
bots-1620466086
• HOW TWITTER BOTS FOOL YOU INTO THINKING THEY ARE REAL PEOPLE: http://www.fastcompany.com/3031500/how-
twitter-bots-fool-you-into-thinking-they-are-real-people
• Rise of the Twitter bots: Social network admits 23 MILLION of its users tweet automatically without human input: http://
www.dailymail.co.uk/sciencetech/article-2722677/Rise-Twitter-bots-Social-network-admits-23-MILLION-users-tweet-
automatically-without-human-input.html
• Twitter Zombies: 24% of Tweets Created by Bots: http://mashable.com/2009/08/06/twitter-bots/
• How bots are taking over the world: http://www.theguardian.com/commentisfree/2012/mar/30/how-bots-are-taking-over-the-
world
• That Time 2 Bots Were Talking, and Bank of America Butted In: http://www.theatlantic.com/technology/archive/2014/07/that-
time-2-bots-were-talking-and-bank-of-america-butted-in/374023/
• The Rise of Twitter Bots: http://www.newyorker.com/tech/elements/the-rise-of-twitter-bots
• OLIVIA TATERS, ROBOT TEENAGER: http://www.onthemedia.org/story/29-olivia-taters-robot-teenager/