A Comparative Study of Deep Learning Models for Fake News Classification

The rapid growth of online media platforms has led to the widespread spread of misinformation, resulting in an important issue which is to correctly categorize fake news to inform citizens effectively, to be a significant issue in the fields of natural language processing (NLP), and social media. Within NLP, deep learning models have become a standard and effective methodology. These models can learn rich linguistic and contextual representations with large datasets. Here contributes a comparative analysis of several Deep Learning model architectures for the identification of fake news: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and transformer-based models like BERT. Also compare all models based on fake news detection datasets and measures, and present their outcomes in terms of accuracy, precision, recall, F1-score, and overall computational efficiency. The analysis revealed the transformer-based models offered the best performance in academic literature due to their contextual awareness in classification, while the RNN and CNN models proffered the best computational efficiency and training times. These findings to highlight the respective advantages and disadvantages that shed light on useful design approaches for the development of effective and operationally efficient fake news detection systems for academic and practitioners alike.