original_docstring:  Applies a threshold to each similarity score to produce a boolean
          indicator.  Fraudulent:True.
              
              Args:
              tweets (1D numpy array): plaintext tweets. 
          
              Returns:
              1D numpy array: true/false values corresponding to fraud/authentic.
          
docstring: Applies a threshold to each similarity score to produce a boolean
  indicator.  Fraudulent:True.
  
  tweets (1D numpy array): plaintext tweets.
  
  1D numpy array: true/false values corresponding to fraud/authentic.
docstring_tokens: Applies a threshold to each similarity score to produce a boolean indicator . Fraudulent : True . tweets ( 1D numpy array ) : plaintext tweets . 1D numpy array : true / false values corresponding to fraud / authentic .
code: def infer(self, tweets):
          """ Applies a threshold to each similarity score to produce a boolean
          indicator.  Fraudulent:True.
              
              Args:
              tweets (1D numpy array): plaintext tweets. 
          
              Returns:
              1D numpy array: true/false values corresponding to fraud/authentic.
          """        
          
          return self.similarity_score(tweets) < self.params['threshold']
code_tokens: def infer ( self , tweets ) : return self . similarity_score ( tweets ) < self . params [ 'threshold' ]
short_docstring: Applies a threshold to each similarity score to produce a boolean
  indicator.
short_docstring_tokens: Applies a threshold to each similarity score to produce a boolean indicator .
comment: """ Applies a threshold to each similarity score to produce a boolean
          indicator.  Fraudulent:True.
              
              Args:
              tweets (1D numpy array): plaintext tweets. 
          
              Returns:
              1D numpy array: true/false values corresponding to fraud/authentic.
          """