Evolving Techniques in Fake News Detection: From Human Expertise to Large Language Models

Abstract

The proliferation of fake news poses significant challenges in today’s digital age. This paper provides a comprehensive review of the most recent advancements in fake news detection methodologies, tracing their evolution from the earliest manual approaches to the latest state- of-the-art models, including large language models. The study identifies four key perspectives: knowledge based, style based, propagation based, and source based. Research on detection methods is classified into manual approaches and automatic approaches utilizing data science techniques like traditional machine learning, deep learning, and large language models. The review highlights the dual role of LLMs in generating and detecting fake news and discusses limitations of current methods, as the lack of datasets, emphasizing the need for multimodal analysis, interdisciplinary collaboration, and improved model transparency.