Year: 2025
Type: article
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.
Author: Stefan Emil Repede
Institution: Lucian Blaga University of Sibiu
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409895047