Comparative Analysis of Deep Learning Techniques for Fake News Detection

The proliferation of fake news has been accelerated by the growth of social media and online news outlets, which poses serious risks to social harmony, political stability, and public confidence. The research examines and compares various deep learning techniques, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and the Bidirectional Encoder Representations from Transformers (BERT) model, to detect fake news. It assesses the effectiveness of these models using a sizable dataset of news reports with authenticity and falsity labels. Important performance parameters including recall, accuracy, precision, and F1-score are considered for analysis. The outcomes show that BERT networks perform better than CNN and LSTM models, with 98.17% accuracy and 98.0% F1-score. The work demonstrates how well BERT captures intricate semantic linkages in text, which makes it a potent instrument for identifying false news. It also offers insights for further research in this area and highlights the power of sophisticated deep learning approaches in improving the effectiveness of false news detection systems.