A Comparative Analysis of Machine Learning and Deep Learning Approaches to Enhanced Fake News Detection
Received: 18 December 2025 | Revised: 16 January 2026 | Accepted: 28 January 2026 | Online: 4 April 2026
Corresponding author: Arshad Ali
Abstract
Fake news influences major aspects of society. To mitigate the dangers of misinformation, manual fact-checking is frequently implemented. Original fake news propagators target innocent individuals to disseminate misleading information. The government and society must first determine the pattern of false news dissemination to address this sequence of events. However, since manually fact-checking the substantial volume of newly generated data is insufficient, machine learning algorithms can be used to detect fake news on different social media platforms. This study applies and evaluates four machine learning algorithms and three deep learning models in detecting fake news. The area under the receiver operating characteristic curve (AUC), precision, F1-score, recall, and accuracy were used to determine the best algorithm for classifying fake news. Logistic Regression (LR) and Long Short-Term Memory (LSTM) both achieved the highest accuracy of 92.97%. This work elucidates the capacity of machine learning and deep learning models to detect fake news.
Keywords:
fake news, machine learning, deep learning, LSTM, NLP, logistic regressionDownloads
References
X. Zhou and R. Zafarani, "A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities," ACM Computing Surveys, vol. 53, no. 5, June 2020, Art. no. 109. DOI: https://doi.org/10.1145/3395046
A. Dhankhar, A. Prakash, and S. Juneja, "Feature Extraction from Text using Grasshopper optimization algorithm for identifying hate speech," in 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), Nov. 2023, pp. 494–498. DOI: https://doi.org/10.1109/ICAICCIT60255.2023.10466147
H. Thakar and B. Bhatt, "Fake news detection: recent trends and challenges," Social Network Analysis and Mining, vol. 14, no. 1, Aug. 2024, Art. no. 176. DOI: https://doi.org/10.1007/s13278-024-01344-4
A. Dhankhar, A. Prakash, S. Juneja, and S. Prakash, "A Survey on Multi-Modal Hate Speech Detection," in 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC), Oct. 2023, pp. 225–230. DOI: https://doi.org/10.1109/R10-HTC57504.2023.10461793
U. Sharma and J. Singh, "A comprehensive overview of fake news detection on social networks," Social Network Analysis and Mining, vol. 14, no. 1, June 2024, Art. no. 120. DOI: https://doi.org/10.1007/s13278-024-01280-3
A. D. Radhi, H. A. H. A. Naffakh, A. I. Fuqdan, B. A. Hakim, and B. Al-Attar, "A comprehensive review of machine learning-based models for fake news detection," BIO Web of Conferences, vol. 97, 2024, Art. no. 00123. DOI: https://doi.org/10.1051/bioconf/20249700123
E. F. Ayetiran and Ö. Özgöbek, "A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection," IEEE Access, vol. 12, pp. 76133–76153, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3406258
I. Saha and S. Puthran, "Fake News Detection: A Comprehensive Review and a Novel Framework," in 2024 OITS International Conference on Information Technology (OCIT), Dec. 2024, pp. 463–469. DOI: https://doi.org/10.1109/OCIT65031.2024.00087
S. I. Manzoor, J. Singla, and Nikita, "Fake News Detection Using Machine Learning approaches: A systematic Review," in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Apr. 2019, pp. 230–234. DOI: https://doi.org/10.1109/ICOEI.2019.8862770
N. Capuano, G. Fenza, V. Loia, and F. D. Nota, "Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review," Neurocomputing, vol. 530, pp. 91–103, Apr. 2023. DOI: https://doi.org/10.1016/j.neucom.2023.02.005
X. Zhang and A. A. Ghorbani, "An overview of online fake news: Characterization, detection, and discussion," Information Processing & Management, vol. 57, no. 2, Mar. 2020, Art. no. 102025. DOI: https://doi.org/10.1016/j.ipm.2019.03.004
N. Aslam, I. Ullah Khan, F. S. Alotaibi, L. A. Aldaej, and A. K. Aldubaikil, "Fake Detect: A Deep Learning Ensemble Model for Fake News Detection," Complexity, vol. 2021, no. 1, 2021, Art. no. 5557784. DOI: https://doi.org/10.1155/2021/5557784
A. Kishwar and A. Zafar, "Fake news detection on Pakistani news using machine learning and deep learning," Expert Systems with Applications, vol. 211, Jan. 2023, Art. no. 118558. DOI: https://doi.org/10.1016/j.eswa.2022.118558
M. Sudhakar and K. P. Kaliyamurthie, "Detection of fake news from social media using support vector machine learning algorithms," Measurement: Sensors, vol. 32, Apr. 2024, Art. no. 101028. DOI: https://doi.org/10.1016/j.measen.2024.101028
S. Hakak, M. Alazab, S. Khan, T. R. Gadekallu, P. K. R. Maddikunta, and W. Z. Khan, "An ensemble machine learning approach through effective feature extraction to classify fake news," Future Generation Computer Systems, vol. 117, pp. 47–58, Apr. 2021. DOI: https://doi.org/10.1016/j.future.2020.11.022
"Fake News Detection Dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/abdelilahelfedg/fake-news-detection-dataset.
J. Alghamdi, Y. Lin, and S. Luo, "A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection," Information, vol. 13, no. 12, Dec. 2022. DOI: https://doi.org/10.3390/info13120576
S. Kumar and B. Arora, "A Review of Fake News Detection Using Machine Learning Techniques," in 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), Aug. 2021, pp. 1–8. DOI: https://doi.org/10.1109/ICESC51422.2021.9532796
A. A. A. Ahmed, A. Aljabouh, P. K. Donepudi, and M. S. Choi, "Detecting Fake News Using Machine Learning : A Systematic Literature Review." arXiv, Feb. 08, 2021.
M. Lahby, S. Aqil, W. M. S. Yafooz, and Y. Abakarim, "Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study," in Combating Fake News with Computational Intelligence Techniques, M. Lahby, A. S. K. Pathan, Y. Maleh, and W. M. S. Yafooz, Eds. Springer International Publishing, 2022, pp. 3–37. DOI: https://doi.org/10.1007/978-3-030-90087-8_1
R. C. Thompson, S. Joseph, and T. T. Adeliyi, "A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection," Information, vol. 13, no. 11, Nov. 2022. DOI: https://doi.org/10.3390/info13110527
O. Bashaddadh, N. Omar, M. Mohd, and M. Nor Akmal Khalid, "Machine Learning and Deep Learning Approaches for Fake News Detection: A Systematic Review of Techniques, Challenges, and Advancements," IEEE Access, vol. 13, pp. 90433–90466, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3572051
D. Choudhury and T. Acharjee, "A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers," Multimedia Tools and Applications, vol. 82, no. 6, pp. 9029–9045, Mar. 2023. DOI: https://doi.org/10.1007/s11042-022-12788-1
W. H. Bangyal et al., "Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches," Computational and Mathematical Methods in Medicine, vol. 2021, no. 1, 2021, Art. no. 5514220. DOI: https://doi.org/10.1155/2021/5514220
I. Q. Abduljaleel and I. H. Ali, "Deep Learning and Fusion Mechanism-based Multimodal Fake News Detection Methodologies: A Review," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15665–15675, Aug. 2024. DOI: https://doi.org/10.48084/etasr.7907
R. Jehad and S. A. Yousif, "Classification of fake news using multi-layer perceptron," AIP Conference Proceedings, vol. 2334, no. 1, Mar. 2021, Art. no. 070004. DOI: https://doi.org/10.1063/5.0042264
B. Xie and Q. Li, "Detecting fake news by RNN-based gatekeeping behavior model on social networks," Expert Systems with Applications, vol. 231, Nov. 2023, Art. no. 120716. DOI: https://doi.org/10.1016/j.eswa.2023.120716
P. Bahad, P. Saxena, and R. Kamal, "Fake News Detection using Bi-directional LSTM-Recurrent Neural Network," Procedia Computer Science, vol. 165, pp. 74–82, Jan. 2019. DOI: https://doi.org/10.1016/j.procs.2020.01.072
A. Jarrahi and L. Safari, "Evaluating the effectiveness of publishers’ features in fake news detection on social media," Multimedia Tools and Applications, vol. 82, no. 2, pp. 2913–2939, Jan. 2023. DOI: https://doi.org/10.1007/s11042-022-12668-8
A. D’Ulizia, M. C. Caschera, F. Ferri, and P. Grifoni, "Fake news detection: a survey of evaluation datasets," PeerJ Computer Science, vol. 7, June 2021, Art. no. e518. DOI: https://doi.org/10.7717/peerj-cs.518
Downloads
How to Cite
License
Copyright (c) 2026 Wesam Ahmed, Arshad Ali, Gahangir Hossain, Mohammad Husain, Amena Mahmoud
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
