Advancements in Fake News Detection Using Machine and Deep Learning Models: Comprehensive Literature Review

In the age of digital technology, the exponential spread of fake news has become a significant issue for society. In response to this issue, considerable advances have been made to identify fake news using machine learning (ML) models. This literature review investigates the current state of research on detecting fake news. It emphasizes the use of ML models such as TF-LIP, Naive Bayes, and Random Forest, as well as deep learning (DL) models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models such as BERT. The review concisely summarizes the essential findings and discusses the potential future implications offake news identification. It also emphasizes the need for additional research to address numerous challenges, such as effective multimedia content management, protection against adversarial attacks, attainment of model generalizability, facilitation of real-time detection, and adherence to ethical standards when developing detection systems. This review is a resource for researchers and practitioners seeking to develop effective methods for addressing the perpetually expanding problem of detecting fake news.