Exploring Machine Learning and Transformer-based Approaches for Deceptive Text Classification: A Comparative Analysis

TL;DR

This paper compares traditional machine learning and transformer-based models like BERT and RoBERTa for deceptive text classification, providing insights into their effectiveness and limitations.

Contribution

It offers a comprehensive comparative analysis of machine learning and transformer models for detecting deceptive text, highlighting their relative strengths and weaknesses.

Findings

Transformer models outperform traditional algorithms in accuracy.

BERT and RoBERTa achieve higher F1 scores.

Limitations of each approach are discussed.

Abstract

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.