Building a Better Lie Detector with BERT: The Difference Between Truth and Lies

Detecting lies or deceptive statements in text is a valuable skill. This is partly because the patterns that underlie deceptive text are not known. The aim of this work is to identify patterns that characterize deceptive text. A key step in this approach is to train a classifier based on the BERT (Bidirectional Encoder Representations from Transformers) network. BERT beats the state of the art in deception classification accuracy on the Ott Deceptive Opinion Spam corpus. The results of our ablation study indicate that certain components of the input, such as some parts of speech, are more informative to the classifier than others. Further part-of-speech analysis in "swing" sentences that are considered important to BERT's classification indicates that deceptive text is more formulaic and less varied than truthful text. We expanded our classifier into a new Generative Adversarial Network based on BERT to create exemplars of deceptive and truthful text that further showed the differences between truth and deception, reinforcing the underlying similarity of deceptive text in terms of part-of-speech makeup.