An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection

An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection

Detecting fake news is a critical challenge in natural language processing (NLP), demanding solutions that balance accuracy, interpretability, and computational efficiency. Despite advances in NLP, systematic empirical benchmarks that directly compare both classical and deep models—across varying in...

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Bibliographic Details
Main Authors: Yexin Tian, Shuo Xu, Yuchen Cao, Zhongyan Wang, Zijing Wei
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Mathematics
Subjects:
fake news detection
natural language processing
machine learning
deep learning
text classification
model interpretability
Online Access:https://www.mdpi.com/2227-7390/13/13/2086
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https://www.mdpi.com/2227-7390/13/13/2086

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