An Intelligent Framework for Automated Fake News Detection
DOI:
https://doi.org/10.66108/mna.v5i02.131Keywords:
Fake News Detection, Machine Learning, TF-IDF, Text Classification, Misinformation Detection, XGBoostAbstract
As digital news platforms and social media grow and multiply, misinformation has become a widespread problem and automatic methods of detecting fake news are becoming more important. This paper introduces a machine learning-based system to detect fake news by applying traditional classification techniques and Text Representation method Term Frequency-Inverse Document Frequency (TF-IDF). The experiments were carried out on the ISOT Fake and True News dataset which has 44,898 news articles including 23,481 fake news and 21,417 real news articles. The articles were pre-processed for each of the articles to convert the title and body in to lower case and then to exclude all the HTML elements, Punctuation, numbers and the stop words from the articles. TF-IDF (with unigram, bigram) was used for the representation of the resulting text. To train the classifiers, the data was split into an 80:20 ratio to develop the set and train the classifiers. The classifiers were trained by dividing the data set into training and testing sets in the ratio of 80:20. The accuracy, precision, recall, F1 score, and confusion matrix analysis were used to evaluate their performance. Among the tested models, precision, recall and F1 Score were 99.79%, 99.91% and 99.85% respectively with test accuracy of 99.86% with XGBoost. The study results reveal that TF-IDF based text representation along with ensemble learning can be a useful method to classify fake news on the chosen data set. As such, the study proposes an automated fake news detection using a machine learning framework with the adoption of traditional classification methods and ensemble techniques.
Downloads
References
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2020). FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data, 8(3), 171–188. https://doi.org/10.1089/big.2020.0062
Khan, J. Y., Khondaker, Md. T. I., Afroz, S., Uddin, G., & Iqbal, A. (2021). A benchmark study of machine learning models for online fake news detection. Machine Learning with Applications, 4, 100032. https://doi.org/10.1016/j.mlwa.2021.100032
Kaliyar, R. K., Goswami, A., & Narang, P. (2021). FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools and Applications, 80(8), 11765–11788. https://doi.org/10.1007/s11042-020-10183-2
Jouhar, J., Pratap, A., Tijo, N., & Mony, M. (2024). Fake News Detection using Python and Machine Learning. Procedia Computer Science, 233, 763–771. https://doi.org/10.1016/j.procs.2024.03.265
Sharma, U., Saran, S., & Patil, S. M. (2020). Fake news detection using machine learning algorithms. International Journal of creative research thoughts (IJCRT), 8(6), 509-518.
Khanam, Z., Alwasel, B. N., Sirafi, H., & Rashid, M. (2021). Fake News Detection Using Machine Learning Approaches. IOP Conference Series: Materials Science and Engineering, 1099(1), 12040. https://doi.org/10.1088/1757-899x/1099/1/012040
Ahmed, H., Traore, I., & Saad, S. (2017). Detecting opinion spams and fake news using text classification. Security and Privacy, 1(1). Portico. https://doi.org/10.1002/spy2.9
Ahmed, H., Traore, I., & Saad, S. (2017). Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, 127–138. https://doi.org/10.1007/978-3-319-69155-8_9
Ahmad, I., Yousaf, M., Yousaf, S., & Ahmad, M. O. (2020). Fake News Detection Using Machine Learning Ensemble Methods. Complexity, 2020, 1–11. https://doi.org/10.1155/2020/8885861
Reis, J. C. S., Correia, A., Murai, F., Veloso, A., & Benevenuto, F. (2019). Explainable Machine Learning for Fake News Detection. Proceedings of the 10th ACM Conference on Web Science, 17–26. https://doi.org/10.1145/3292522.3326027
Kumar, S., Asthana, R., Upadhyay, S., Upreti, N., & Akbar, M. (2019). Fake news detection using deep learning models: A novel approach. Transactions on Emerging Telecommunications Technologies, 31(2). Portico. https://doi.org/10.1002/ett.3767
Wang, Y., Yang, W., Ma, F., Xu, J., Zhong, B., Deng, Q., & Gao, J. (2020). Weak Supervision for Fake News Detection via Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 516–523. https://doi.org/10.1609/aaai.v34i01.5389
Ibrishimova, M. D., & Li, K. F. (2019). A Machine Learning Approach to Fake News Detection Using Knowledge Verification and Natural Language Processing. Advances in Intelligent Networking and Collaborative Systems, 223–234. https://doi.org/10.1007/978-3-030-29035-1_22
Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid CNN-RNN based deep learning approach. International Journal of Information Management Data Insights, 1(1), 100007. https://doi.org/10.1016/j.jjimei.2020.100007
Alghamdi, J., Luo, S., & Lin, Y. (2023). A comprehensive survey on machine learning approaches for fake news detection. Multimedia Tools and Applications, 83(17), 51009–51067. https://doi.org/10.1007/s11042-023-17470-8
Alghamdi, J., Lin, Y., & Luo, S. (2022). A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection. Information, 13(12), 576. https://doi.org/10.3390/info13120576
Farhangian, F., Cruz, R. M. O., & Cavalcanti, G. D. C. (2024). Fake news detection: Taxonomy and comparative study. Information Fusion, 103, 102140. https://doi.org/10.1016/j.inffus.2023.102140
Hamed, S. K., Ab Aziz, M. J., & Yaakub, M. R. (2023). A review of fake news detection approaches: A critical analysis of relevant studies and highlighting key challenges associated with the dataset, feature representation, and data fusion. Heliyon, 9(10), e20382. https://doi.org/10.1016/j.heliyon.2023.e20382
Saeed, A., & Solami, E. A. (2023). Fake News Detection Using Machine Learning and Deep Learning Methods. Computers, Materials &Amp; Continua, 77(2), 2079–2096. https://doi.org/10.32604/cmc.2023.030551
Additional Files
Published
How to Cite
License
Copyright (c) 2026 Ayesha Maryam, Faisal Shahzad
This work is licensed under a Creative Commons Attribution 4.0 International License.
© This work is published by Machines and Algorithms and licensed under the terms of Creative Commons Attribution 4.0 International License (CC BY 4.0).
