Fake News Detection: A Survey of Machine Learning, Deep Learning, and Transformer-Based Approaches

Abstract

Artificial intelligence, social media, and online news platforms have all experienced tremendous growth in recent years, which has led to a considerable increase in the dissemination of fake news. This has resulted in severe problems for public confidence, democracy, healthcare, and economic stability. This is because of the extensive spread of misinformation and disinformation, which makes it increasingly difficult to differentiate between genuine news and content that has been manufactured. As a consequence of this, the detection of fake news through automated systems has emerged as a significant area of research in the fields of artificial intelligence and natural language processing. In this article, a complete overview of various ways for detecting false news is presented. These techniques include rule-based methods, machine learning, deep learning, transformer-based models, large language models (LLMs), multimodal learning, and hybrid approaches. Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) are examples of traditional machine learning algorithms that offer efficient baseline performance. On the other hand, deep learning models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM) significantly enhance contextual understanding. The detection accuracy of recent transformer architectures such as BERT, RoBERTa, ALBERT, and GPT has been significantly improved with the implementation of advanced language representation and contextual learning. More robustness and dependability can be achieved by the utilisation of multimodal and ensemble methodologies, which mix information from textual, visual, and social network sources. In spite of these achievements, there are still substantial difficulties that need to be addressed, including multilingual material, misinformation generated by artificial intelligence, limited labelled datasets, explainability, and computational complexity. In light of this, it is recommended that future research concentrate on the development of frameworks for the identification of fake news that are resilient, explainable, and computationally efficient. These frameworks should be able to handle growing misinformation across numerous digital platforms.