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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Summary: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 input richness and with careful attention to interpretability and computational tradeoffs—remain underexplored. In this study, we systematically evaluate the mathematical foundations and empirical performance of five representative models for automated fake news classification: three classical machine learning algorithms (Logistic Regression, Random Forest, and Light Gradient Boosting Machine) and two state-of-the-art deep learning architectures (A Lite Bidirectional Encoder Representations from Transformers—ALBERT and Gated Recurrent Units—GRUs). Leveraging the large-scale WELFake dataset, we conduct rigorous experiments under both headline-only and headline-plus-content input scenarios, providing a comprehensive assessment of each model’s capability to capture linguistic, contextual, and semantic cues. We analyze each model’s optimization framework, decision boundaries, and feature importance mechanisms, highlighting the empirical tradeoffs between representational capacity, generalization, and interpretability. Our results show that transformer-based models, especially ALBERT, achieve state-of-the-art performance (macro F1 up to 0.99) with rich context, while classical ensembles remain viable for constrained settings. These findings directly inform practical fake news detection.
ISSN:2227-7390

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