  Deceptive text classification is a critical task in natural language
processing that aims to identify deceptive o fraudulent content. This study
presents a comparative analysis of machine learning and transformer-based
approaches for deceptive text classification. We investigate the effectiveness
of traditional machine learning algorithms and state-of-the-art transformer
models, such as BERT, XLNET, DistilBERT, and RoBERTa, in detecting deceptive
text. A labeled dataset consisting of deceptive and non-deceptive texts is used
for training and evaluation purposes. Through extensive experimentation, we
compare the performance metrics, including accuracy, precision, recall, and F1
score, of the different approaches. The results of this study shed light on the
strengths and limitations of machine learning and transformer-based methods for
deceptive text classification, enabling researchers and practitioners to make
informed decisions when dealing with deceptive content.
