Fake News Detection Using Machine Learning and Deep Learning: A Critical Review
Research has highlighted the proliferation of fake news via social media, threatening societal aspects such as politics, economics, and social stability. This review aims to evaluate the effectiveness of machine learning and deep learning algorithms in detecting fake news and identify areas for future research.
Key Takeaways:
- The research found that the proportion of fake news has increased significantly due to rapid access to news without considering its reliability.
- Machine learning and deep learning are crucial for fake news detection, providing a comparison and discussion of their usage in this context.
- The review excluded articles that did not demonstrate the use of algorithms or performance, indicating the importance of evaluating effectiveness in fake news detection research.
- The most commonly used publishers in this field are IEEE, Intelligent Systems, EMNLP, ACM, Springer, Elsevier, JAIR, and others.
- The review focused on conducting a comprehensive review and evaluation of fake news detection research, exploring future perspectives and gaps in the field.
- The research questions addressed included the importance of machine learning and deep learning for fake news detection.
Statistics:
- 2018-2025: The timeframe in which the review results were presented.
- 14(9):394: The journal article classification for Fake News Detection Using Machine Learning and Deep Learning Algorithms: A Comprehensive Review and Future Perspectives.
- 11543: The postal code of Riyadh, Saudi Arabia, where King Saud University is located.
- MDPI AG: The publisher of the journal Computers.
- 10.3390/computers14090394: The DOI for the research article.
Sources:
- Fake News Detection Using Machine Learning and Deep Learning Algorithms: A Comprehensive Review and Future Perspectives. Computers, 2025,14(9):394.
- NewsRx. Research on Machine Learning Discussed by Researchers at King Saud University (Fake News Detection Using Machine Learning and Deep Learning Algorithms: A Comprehensive Review and Future Perspectives). Journal of Engineering. October 13, 2025; p 3544.