What's the proper Word2vec model to get pre-trained word embedding for a classification task?
I think this article gives general idea of pros / cons between CBOW and Skip Gram,

Link


  According to Mikolov:
  
  Skip-gram: works well with small amount of the training data,
  represents well even rare words or phrases.
  
  CBOW: several times faster
  to train than the skip-gram, slightly better accuracy for the frequent
  words.


So without looking at data, assuming that you have large amount of data, CBOW might be my bet. However, if you have labels for "deceptive" and "truthful", it will be best to train both models, and use whichever outperforms via cross-validation.