Fake News Detection: A Review of Machine Learning and Deep Learning Methods
DOI:
https://doi.org/10.54097/5qcg7h54Keywords:
Fake news detection, machine learning, deep learning, information authenticity, natural language processing.Abstract
Fake news is spreading quickly in the digital era, endangering media trust and social cognition. The Internet and social media platforms have made information spread rapidly, but they have also led to a large number of misleading contents, some plantforms keeping publish news with wrong information to the public. This widespread false information distorts the public's understanding of the event, and it threatens social stability. The manual identification of true and false news faces enormous challenges, mainly due to the large amount and diverse forms of online information. Therefore, developing an automated fake news detection system has become necessary. With an emphasis on machine learning and deep learning techniques, this article reviews the state of the art in false news detection research. Several approaches about fake news detection are included in this article. The performance of various models in experiments is covered, along with important elements including feature extraction, classification techniques, and performance evaluation.
References
[1] I. Ahmad, M. Yousaf, S. Yousaf, & M. O. Ahmad. Fake News Detection Using Machine Learning Ensemble Methods. Computational Intelligence and Neuroscience, 8885861. (2020).
[2] S. I. Manzoor, J. Singla, et al. Fake News Detection Using Machine Learning Approaches: A Systematic Review. In *2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)* (pp. 230–234). IEEE. (2019).
[3] N. F. Baarir, & A. Djeffal. Fake news detection using machine learning. In 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH)* (pp. 125–130). IEEE. (2021). https://doi.org/10.1109/IHSH51661.2021.9378748
[4] S. Chitti, D. R. Rinku, T. K. Juluru, P. R. Rao, & S. Gouthami. Identifying hoaxes in fake spotter using XG boost machine learning based classification method. In *2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)* (pp. 990-996). IEEE. (2024). https://doi.org/10.1109/ICUIS64676.2024.10866064
[5] P. Bailke, T. Kulkarni, Y. Kulkarni, A. Kulkarni, A. Jadhav, & D. Jadhav. Advanced fake news detection: Multi-lingual analysis and image forgery detection integration. In *2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT)* (pp. 321-326). IEEE. (2025). https://doi.org/10.1109/CSNT64827.2025.10969051
[6] S. Xiong, T. Huang, H. Xie, & J. Shen. Sentence-guided comment tree fusion for fake news detection. In *2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)* (pp. 32-35). IEEE. (2024). https://doi.org/10.1109/ICAIRC64177.2024.10900273
[7] M. M. T. Ayyalasomayajula, S. Bussa, S. S. N. Kowsalya, N. Mishra, S. Prasad, & A. Mehra. Leveraging feature extraction and fine-tuning techniques for enhanced detection of fake news using BERT. In *2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N) * (pp. 1630–1635). IEEE. (2024). https://doi.org/10.1109/ICAC2N63387.2024.10895840
[8] K. J. Kumar, K. Shreya, L. P. Divakarla, P. C. Nair, & N. Sampath. Interpretable AI insights in fake news detection: A comparative analysis of CNN and LSTM. In *2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)* (pp. 1–6). IEEE. (2024). https://doi.org/10.1109/ICCCNT61001.2024.10724705
[9] Y. Djenouri, A. N. Belbachir, T. Michalak, & G. Srivastava. A federated convolution transformer for fake news detection. IEEE Transactions on Big Data, 10(3), 214–225. (2024). https://doi.org/10.1109/TBDATA.2023.3325746
[10] A. K. Srivastav, V. Ranga, & D. K. Vishwakarma. Machine learning and deep learning in fake news detection: An in-depth review. In *2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N) * (pp. 898–903). IEEE. (2024). https://doi.org/10.1109/ICAC2N63387.2024.10894957
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
