DOI:
https://doi.org/10.64539/sjcs.v2i1.2026.400Keywords:
Fake news, CNN, GRU, CNN-GRU, Deep learningAbstract
The spread of fake news on social media platforms has created a dilemma for the world community by spreading false information and eroding public confidence. Fake news spreads quickly and seriously harms society. Predicting and identifying fake news is crucial for preserving the integrity of information ecosystems in the wake of an epidemic of multiple high-profile disinformation efforts. In order to detect fake news, this work suggests a hybrid deep learning algorithm called Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU), which combines the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) learning algorithms in an efficient manner. Models for identifying fake news were developed using deep learning-based methods, such as CNN, GRU, and CNN-GRU deep learning algorithms. Four standard performance metrics—accuracy, precision, recall, and F1-score—were used to evaluate the models. Nevertheless, the CNN-GRU deep learning-based detection model outperformed models created with CNN and GRU, achieving the maximum accuracy of 98.77%, 98.68%, 98.73%, and 98.71% for precision, recall, and F1-score, respectively. With a combined accuracy of 98.77%, precision of 98.68%, recall of 98.73%, and F1-score of 98.71%, the CNN-GRU deep learning-based false news detection model performs better than the two other deep learning-based models.
References
[1] A.O. Alsayed et al, “Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques”. Appl. Sci. Vol. 11, no. 22, art. No. 10639, 2021. https://doi.org/10.3390/app112210639.
[2] P. Jiang, Y. Chen, B. Liu, D. He and C. Liang, "Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks," IEEE Access, vol. 7, pp. 59069 – 59080, 2019. https://doi.org/10.1109/ACCESS.2019.2914929.
[3] L. J. Muhammad, A. A. Haruna, U. S. Sharif, M. P. Mohammed, “CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana,” Health and Technology, vol 12, pp. 1259–1276 2022. https://doi.org/10.1007/s12553-022-00711-5.
[4] S. Sreejith, K. Nehemiah, A. Kannan, “A. Clinical data classification using an enhanced SMOTE and chaotic evolutionary feature selection,” Computers in Biology and Medicine, vol. 126, art no. 103991. 2020. https://doi.org/10.1016/j.compbiomed.2020.103991.
[5] J. A. Nasir, O. S. Khan, I. Varlamis “Fake news detection: A hybrid CNN-RNN based deep learning approach”. International journal of information management data insights, vol. 1, no. 1, 2021. https://doi.org/10.1016/j.jjimei.2020.100007.
[6] F. I. Adiba, T. Islam, M. S. Kaiser, M. Mahmud, M. A. Rahman, "Effect of Corpora on Classification of Fake News using Naive Bayes Classifiers," International Journal of Automation, Artificial Intelligence and Machine Learning, vol. 1, no. 1, pp. 80-92, 2020. https://doi.org/10.61797/ijaaiml.v1i1.45.
[7] R. R. Karwa, S. R. Gupta, "Automated Hybrid Deep Neural Network Model for Fake News Identification and Classification in Social Networks," Journal of Integrated Science and Technology, vol. 10, no. 2, pp. 110-119, 2022, https://doi.org/10.5875/ausmt.v13i1.2409.
[8] E. Tacchini, G. Ballarin, M. L. Della Vedova, S. Moret, and L. de Alfaro, "Some like it hoax: Automated fake news detection in social networks," arXiv preprint arXiv:1704.07506, 2017. https://doi.org/10.48550/arXiv.1704.07506.
[9] L. Alzubaidi et al., "A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications," Journal of Big Data, vol. 10, Article no. 46, 2023. https://doi.org/10.1186/s40537-023-00727-2.
[10] E. D. Ajik, G. N. Obuandike, F. O. Echobu, "Fake News Detection Using Optimized CNN and LSTM Techniques," Journal of Information Systems and Informatics, vol. 5, no. 3, pp. 1044-1057, 2023, https://doi.org/10.51519/journalisi.v5i3.548.
[11] F. W. R. Tokpa, B. H. Kamagaté, V. Monsan, S. Oumtanaga, "Fake news detection in social media: Hybrid deep learning approaches," Journal of Advances on Information Technology, vol. 14, no. 3, pp. 606-615, 2023. https://doi.org/10.12720/jait.14.3.606-615.
[12] F. C. Akyon and M. E. Kalfaoglu, "Instagram fake and automated account detection," in Proc. Innovations in Intelligent Syst. Applications Conf. (ASYU), 2019. https://doi.org/10.1109/ASYU48272.2019.8946437.
[13] M. Aldwairi and A. Alwahedi, "Detecting fake news in social media networks," Procedia Computer Science, vol. 141, pp. 215-222, 2018. https://doi.org/10.1016/j.procs.2018.10.171.
[14] H. Allcott and M. Gentzkow, "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, vol. 31, no. 2, pp. 211-236, May 2017, https://doi.org/10.1257/jep.31.2.211.
[15] M. Akay, Y. M. Akay, and W. Wolfowitz, "Neural networks for the diagnosis of coronary artery disease," in Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), 1992, vol. 2, pp. 419-424. https://doi.org/10.1109/IJCNN.1992.226952.
[16] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998. https://doi.org/10.1109/5.726791.
[17] F. S. Prity et al., “RvXmBlendNet: A multi-architecture hybrid model for improved skin cancer detection,” Human-Centric Intelligent Systems, vol. 4, pp. 545–570, 2024. https://doi.org/10.1007/s44230-024-00083-1.
[18] O. A. Sarumi, O. Aouedi, L. J. Muhammad. “Potential of Deep Learning Algorithms in Mitigating the Spread of COVID-19”. In: J. Nayak, B. Naik, A. Abraham (eds) Understanding COVID-19: The Role of Computational Intelligence. Studies in Computational Intelligence, vol 963, 2022. https://doi.org/10.1007/978-3-030-74761-9_10.
[19] A. Bhagat, D. Dangi, V. Suman, D. K. Dixit, S. Sharma, “Fake News Detection Using ARO and LSTM Algorithms,” SN Computer Science, vol. 6, p. 36, 2025. https://doi.org/10.1007/s42979-024-03574-x.
[20] B. A. Tanawala and D. C. Dalwadi, “Harmonic synergy: Leveraging deep convolutional networks, long short-term memory networks, and recurrent neural networks for multi-genre piano roll generation with generative adversarial networks,” SN Computer Science, vol. 6, p. 297, 2025. https://doi.org/10.1007/s42979-025-03855-z.
[21] C. S. Lakshmi, S. Saxena, and K. B. Kumar, “Sentiment analysis and classification of COVID-19 tweets using machine learning classifier,” Journal of Autonomous Intelligence, vol. 7, no. 2, 2024.
[22] D. K. Dixit, A. Bhagat, and D. Dangi, “Automating fake news detection using PPCA and levy flight-based LSTM,” Soft Computing, vol. 26, no. 22, pp. 12545–12557, 2022. https://doi.org/10.1007/s00500-022-07215-4.
[23] S. Raza and C. Ding, “Fake news detection based on news content and social contexts: A transformer-based approach,” International Journal of Data Science and Analytics, vol. 13, no. 4, pp. 335–362, 2022. https://doi.org/10.1007/s41060-021-00302-z.
[24] M. S. Hossain, L. J. Armstrong, D. M. Cook, P. Zaenker, “Application of histopathology image analysis using deep learning networks,” Human-Centric Intelligent Systems, vol. 4, pp. 417–436, 2024. https://doi.org/10.1007/s44230-024-00077-z.
[25] Q. Chang, X. Li, and Z. Duan, “Graph global attention network with memory: A deep learning approach for fake news detection,” Neural Networks, vol. 172, p. 106115, 2024. https://doi.org/10.1016/j.neunet.2024.106115.
[26] B. Gülmez. “Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm,” Expert Systems with Applications. vol. 227, art. no. 120346, 2023. https://doi.org/10.1016/j.eswa.2023.120346.
[27] H. Ahmed, I. Traore, and S. Saad, "Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques," in International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, 2017, pp. 127-138. https://doi.org/10.1007/978-3-319-69155-8_9.
[28] K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1724-1734. https://aclanthology.org/D14-1179.pdf.
[29] E. Lesyk, T. Arias-Vergara, E. Nöth, A. Maier, J. R. Orozco-Arroyave, and P. A. Perez-Toro., “Empathetic deep learning: Transferring adult speech emotion models to children with gender-specific adaptations using neural embeddings,” Human-Centric Intelligent Systems, vol. 4, pp. 633–642, 2024. https://doi.org/10.1007/s44230-024-00088-w.
[30] W. Romsaiyud, H. Schnoor, and W. Hasselbring “Improving k-nearest neighbor pattern recognition models for privacy-preserving data analysis”. In 2019 IEEE International Conference on Big Data (Big Data), pp. 5804-5813, 2019. https://doi.org/10.1109/BigData47090.2019.9006281.
[31] I. AlShourbaji, P. Kachare, W. Zogaan,, L. J. Muhammed, and L. Abualigah, “Learning features using an optimized artificial neural network for breast cancer diagnosis”. SN Computer Science, vol. 3, art. No. 229, 2022. https://doi.org/10.1007/s42979-022-01129-6.
[32] K. Shu, et al., “FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media,” Big Data, vol. 8, no. 3, pp. 171–188, 2020. https://doi.org/10.1089/big.2020.0062.
[33] D. Jyoti, D. P. Mahato, and J. Srivastava, “Deep learning-based scientific document summarization considering citation,” SN Computer Science, vol. 6, art. No. 300, 2025. https://doi.org/10.1007/s42979-025-03821-9.
[34] D. Trandabăț and D. Gifu, “Discriminating AI-generated fake news,” Procedia Computer Science, vol. 225, pp. 3822–3831, 2023. https://doi.org/10.1016/j.procs.2023.10.378.
[35] R. Kailasam and S. Balasubramanian, “Deep learning for pneumonia detection: A combined CNN and YOLO approach,” Human-Centric Intelligent Systems, vol. 5, pp. 44–62, 2025. https://doi.org/10.1007/s44230-025-00091-9.
[36] A. Hanson, K. PNVR, S. Krishnagopal, L. Davis, “Bidirectional Convolutional LSTM for the Detection of Violence in Videos” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018.
[37] I. Ahmad, M. Yousaf, S. Yousaf, and M. O. Ahmad, “Fake news detection using machine learning ensemble methods,” Complexity, vol. 2020, no. 1, p. 8885861, 2020. https://doi.org/10.1155/2020/8885861.
[38] P. Samuels, “Exploring cultural aspects of visual metaphors in doctoral and early career researcher training,” in Proceedings of the 4th International Technical University of Kenya Exploring Visual Cultures Conference, 2023. https://www.researchgate.net/profile/Peter-Samuels/publication/388315400.
[39] M. Bouchane, W. Guo, and S. Yang, “Hybrid CNN-GRU models for improved EEG motor imagery classification,” Sensors, vol. 25, no. 5, p. 1399, 2025. https://doi.org/10.3390/s25051399.
[40] N. Pachhala, S. Jothilakshmi, and B. P. Battula, “Cross-Platform Malware Classification: Fusion of CNN and GRU Models,” International Journal of Safety and Security Engineering, vol. 14, no. 2, pp. 477–486, 2024. https://doi.org/10.18280/ijsse.140215.
[41] R. Puthran, “Spatio-temporal analysis of hybrid convolutional neural network and gated recurrent unit model for prediction of earthquake for disaster management,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 3s, pp. 270–281, 2024. https://ijisae.org/index.php/IJISAE/article/view/3705.
[42] J. Yu, X. Zhang, L. Xu, J. Dong, and L. Zhangzhong, “A hybrid CNN-GRU model for predicting soil moisture in maize root zone,” Agricultural Water Management, vol. 245, p. 106649, 2021. https://doi.org/10.1016/j.agwat.2020.106649.
[43] A. Golovin, “Fake News: Fake News dataset based on FakeNewsNet,” [Online]. Available: https://www.kaggle.com/datasets/algord/fake-news.
