A Review of Automatic Fake News Detection: From Traditional Methods to Large Language Models
Year: 2025
Type: article
Abstract: In the current digital era, the spread of fake news presents serious difficulties. This study offers a thorough analysis of recent developments in false news automatic detection techniques, from traditional methods to the most recent developed models like large language models. The review identifies four perspectives on automatic detection of fake news that are oriented towards knowledge, style, propagation, and source of the misinformation. This paper describes how automatic detection methods use data science techniques such as deep learning, large language models, and traditional machine learning. In addition to discussing the shortcomings of existing approaches, such as the absence of datasets, this paper emphasizes the multidimensional function of large language models in creating and identifying fake news while underlining the necessity for textual, visual, and audio common analysis, multidisciplinary collaboration, and greater model transparency.
Source: Future Internet
DOI: 10.3390/fi17100435
Authors: Repede Ștefan Emil, Brad Remus
Institution: Lucian Blaga University of Sibiu
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4414530057