A Comprehensive Review on Fake News Detection using Deep Learning Models

A big problem today is that organizations from different areas are having a hard time finding good ways to find fake news online. It's tricky to tell real information from fake because fake news is often made to trick people Deep learning beats all the other methods in finding fake news accurately. Most of the previous reviews dealt only with a data mining perspective and not the use of deep learning methods for fake news detection. Techniques such as Attention, Generative Adversarial Networks, and Bidirectional Encoder Representation are still new and have not been included in previous studies. This study probes for Transformers the newest and most excellent methods of detecting fake news using deep learning. First, we explain the harm brought by fake news. Next is a discussion of the datasets used in previous studies, along with the NLP approaches they employed. We give a copious account of deep learning methods in order to cluster their usage into various categories. We also provide the principal ways by which the accuracy of fake news detection is measured. Other suggestions to improve fake news detection systems in future research are also given. Precision metrics demonstrate the effectiveness of the models ranging from 0.76 to 0.97 across the various models. In particular, the accuracy for true and fake news classification touches 0.97 for LSTM, Bidirectional LSTM, and GRU models. Lessons learned from these benchmarks produce a comparative analysis of the present unreliable-fact detection techniques while evaluating the pros and cons of the deep learning models. This research makes a significant contribution to developing robust mechanisms to combat misinformation while not compromising the online information ecosystems.