Advanced machine learning techniques for fake news detection: A comprehensive analysis
1 Department of Information Technology, Washington University of Science and Technology, USA.
2 Department of ERP SAP Business Analytics, Maharishi International University, USA.
3 Department of Information Technology System & Management, Washington University of Science and Technology, USA.
4 Department of Computer Science and Engineering, Atish Dipankar University of Science and Technology, Bangladesh.
5 Department of Computer Science and Engineering, Daffodil International University, Bangladesh.
6 Department of Computer Science and Engineering, American International University, Bangladesh.
Research Article
Magna Scientia Advanced Research and Reviews, 2024, 12(02), 203–212
Article DOI: 10.30574/msarr.2024.12.2.0198
Publication history:
Received on 19 October 2024; revised on 30 November 2024; accepted on 02 December 2024
Abstract:
The rise of fake news has become a significant global concern, undermining public trust and information integrity. This study explores the application of advanced machine learning algorithms for detecting fake news, leveraging a balanced dataset of real and fake news articles. Through rigorous preprocessing, including text cleaning and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, the study enhances data quality and model performance. Five machine learning models—Random Forest, Support Vector Machine (SVM), Neural Networks, Logistic Regression, and Naïve Bayes—are systematically evaluated using metrics such as accuracy, precision, recall, and F1-score. Results indicate that the Random Forest Classifier outperforms other models with an accuracy of 99.95% and balanced performance across metrics, demonstrating its robustness in distinguishing fake from real news. SVM and Neural Networks also achieve high accuracy, showcasing their capability in handling complex data. Logistic Regression and Naïve Bayes, while computationally efficient, exhibit relatively lower performance. The findings underscore the importance of ensemble methods and sophisticated preprocessing techniques in detecting fake news effectively. This research provides a methodological framework for scalable fake news detection, offering valuable insights for developing automated systems to combat misinformation and promote informed decision-making in the digital age.
Keywords:
Fake news detection; News detection; Machine learning; Text classification; Natural language processing
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0