@inproceedings{sadfiComparativeAnalysisMachine2026,

title = {Comparative Analysis of Machine Learning, LLMs, and RAG for Fake News Detection},

author = {Y. Sadfi and H. Amamou and A. Davoust and A. R. Avila},

url = {https://www.scopus.com/pages/publications/105042025800?origin=resultslist},

doi = {10.1109/CAI68641.2026.11536343},

isbn = {979-833156039-3 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {IEEE Conf. Artif. Intell., CAI},

pages = {2128–2133},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The rapid spread of misinformation on social network platforms poses a serious threat to the decision-making processes in democratic societies. To mitigate the problem and cope with new misinformation scenarios several datasets have been proposed, followed by the emergence of new methods for detecting fake news. While several comparative analyses have been proposed to benchmark these solutions, the ever-growing development of new methods requires up-to-date analysis. For instance, recent approaches based on retrieval-augmented generation (RAG) have yet to be assessed and compared to previous methods used for fake news detection. In this study, we aim to fill this gap by presenting a comprehensive assessment of classical machine learning (ML) algorithms, Transformer-based models, such as BERT, large language models (LLMs), and RAG-based classifiers. We adopt four traditional fake news datasets, including a Portuguese one. Results indicate that classical ML and BERT deliver strong performance on in-domain tasks, demonstrating efficiency and high accuracy. RAG-augmented LLMs, on the other hand, offer enhanced generalization and robustness in cross-domain and cross-dataset settings, but are outperformed by traditional approaches for in-domain evaluation. © 2026 IEEE.},

note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},

keywords = {Classification (of information), Comparative analyzes, Comprehensive assessment, decision making, Decision-making process, Fake detection, Generalisation, High-accuracy, Language model, Learning algorithms, Learning systems, Machine learning, Machine learning algorithms, Machine-learning, Network platforms, Performance},

pubstate = {published},

tppubtype = {inproceedings}

}