new_contents: import re
  from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
  from sklearn import cross_validation
  from sklearn.cross_validation import KFold
  
  #read the text file of polluters information
  reader_1 = open("social_honeypot_icwsm_2011\content_polluters.txt", 'r')
  
  #convert the content read into usable dataset
  polluters = []
  for line in reader_1:
      tokens = re.split("[\t\n]", line)
      #parse strings to numbers, take out the dates
      parsed_tokens = [int(r) for r in ([tokens[0]] + tokens[3:8])]
      polluters.append(parsed_tokens)
  
  #read the file of legitimate users information, and convert it into usable dataset
  reader_2 = open("social_honeypot_icwsm_2011\legitimate_users.txt", 'r')
  legitimate_users = []
  for line in reader_2:
      tokens = re.split("[\t\n]", line)
      parsed_tokens = [int(r) for r in ([tokens[0]] + tokens[3:8])]
      legitimate_users.append(parsed_tokens)
  
  #some index numbers of specific information for future use
  userID = 0
  createdAt = 1
  collectedAt = 2
  numberOfFollowings = 3
  numberOfFollowers = 4
  numberOfTweets = 5
  lengthOfScreenName = 6
  lengthOfDescriptionInUserProfile = 7
  
  #combine 2 datasets for classification, dataset_Y is the target list
  #the data will be shuffled before classify
  dataset_X = polluters + legitimate_users
  dataset_Y = [0] * len(polluters) + [1] * len(legitimate_users)
  
  """
  #How to implement cross validation by LogisticRegressionCV?
  
  kFold = KFold(n = len(dataset_X), n_folds = 10, shuffle = True)
  lr = LogisticRegressionCV(cv = kFold)
  scores_1 = lr.score(dataset_X, dataset_Y)
  print(scores_1)
  """
  
  #need KFold iterator since method "cross_val_score" doesn't provide shuffling function
  kFold = KFold(n = len(dataset_X), n_folds = 10, shuffle = True)
  
  #build the classifier and do classification
  lr = LogisticRegression()
  scores_2 = cross_validation.cross_val_score(lr, dataset_X, dataset_Y, cv = kFold)
  print(scores_2)
  
  
  
subject: Create client.py