An Improved Ensemble Framework for Social Media Fake News Detection Using RoBERTa Embeddings and the Whale Optimization Algorithm
Keywords:
Fake news detection, RoBERTa embeddings, ensemble learning, Whale Optimization Algorithm, evaluation metrics, LIAR dataset, ISOT datasetAbstract
The increasing prevalence of fake news on social media has raised serious concerns due to its impact on public perception and decision-making. In response, this study introduces a hybrid ensemble learning framework enhanced by RoBERTa-based feature representation and optimized using the Whale Optimization Algorithm (WOA). The proposed approach aims to effectively capture deep semantic patterns in textual data while improving classification performance through adaptive parameter tuning. RoBERTa is employed to generate high-quality textual embeddings from news content, which are then fed into a set of base classifiers to ensure robustness and diversity in predictions. The WOA algorithm fine-tunes the ensemble model parameters, resulting in improved convergence and reduced error rates. The model was evaluated using two well-known fake news datasets, LIAR and ISOT. On the LIAR dataset, the proposed method achieved an accuracy of 98.17%, precision of 98.1%, recall of 97.8%, and an F1-score of 97.9%. On the ISOT dataset, it achieved an accuracy of 99.24%, precision of 99.3%, recall of 98.9%, and an F1-score of 99.1%. These results confirm the high reliability and balanced performance of the framework in distinguishing between true and false information across diverse content.
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Abualigah, L., Al-Ajlouni, Y. Y., Daoud, M. S., Altalhi, M., & Migdady, H. (2024). Fake news detection using recurrent neural network based on bidirectional LSTM and GloVe. Social Network Analysis and Mining, 14(1), 40. https://doi.org/10.1007/s13278-024-01198-w
Adedoyin, F., & Mariyappan, B. (2024). Fake News Detection using Machine Learning Algorithms and Recurrent Neural Networks.
Ahmad, T., Faisal, M. S., Rizwan, A., Alkanhel, R., Khan, P. W., & Muthanna, A. (2022). Efficient fake news detection mechanism using enhanced deep learning model. Applied Sciences, 12(3), 1743. https://doi.org/10.3390/app12031743
Al Ghamdi, M. A., Bhatti, M. S., Saeed, A., Gillani, Z., & Almotiri, S. H. (2024). A fusion of BERT, machine learning and manual approach for fake news detection. Multimedia Tools and Applications, 83(10), 30095-30112. https://doi.org/10.1007/s11042-023-16669-z
Alghamdi, J., Luo, S., & Lin, Y. (2024). 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
Alsuwat, E., & Alsuwat, H. (2025). An improved multi-modal framework for fake news detection using NLP and Bi-LSTM. Journal of Supercomputing, 81(1), 177. https://doi.org/10.1007/s11227-024-06671-z
Capuano, N., Fenza, G., Loia, V., & Nota, F. D. (2023). Content Based Fake News Detection with machine and deep learning: a systematic review. Neurocomputing. https://doi.org/10.1016/j.neucom.2023.02.005
Choudhury, D., & Acharjee, T. (2023). A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers. Multimedia Tools and Applications, 82(6), 9029-9045. https://doi.org/10.1007/s11042-022-12788-1
Cui, W., & Shang, M. (2025). MIGCL: Fake news detection with multimodal interaction and graph contrastive learning networks. Applied Intelligence, 55(1), 1-23. https://doi.org/10.1007/s10489-024-05883-3
Del Vicario, M., Quattrociocchi, W., Scala, A., & Zollo, F. (2019). Polarization and fake news: Early warning of potential misinformation targets. Acm Transactions on the Web, 13(2), 1-22. https://doi.org/10.1145/3316809
Elyassami, S., Alseiari, S., AlZaabi, M., Hashem, A., & Aljahoori, N. (2022). Fake news detection using ensemble learning and machine learning algorithms. In Combating Fake News with Computational Intelligence Techniques (pp. 149-162). https://doi.org/10.1007/978-3-030-90087-8_7
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
Farokhian, M., Rafe, V., & Veisi, H. (2024). Fake news detection using dual BERT deep neural networks. Multimedia Tools and Applications, 83(15), 43831-43848. https://doi.org/10.1007/s11042-023-17115-w
Fayaz, M., Khan, A., Bilal, M., & Khan, S. U. (2022). Machine learning for fake news classification with optimal feature selection. Soft Computing, 26(16), 7763-7771. https://doi.org/10.1007/s00500-022-06773-x
Goldani, M. H., Momtazi, S., & Safabakhsh, R. (2021). Detecting fake news with capsule neural networks. Applied Soft Computing, 101, 106991. https://doi.org/10.1016/j.asoc.2020.106991
Hakak, S., Alazab, M., Khan, S., Gadekallu, T. R., Maddikunta, P. K. R., & Khan, W. Z. (2021). An ensemble machine learning approach through effective feature extraction to classify fake news. Future Generation Computer Systems, 117, 47-58. https://doi.org/10.1016/j.future.2020.11.022
Hashmi, E., Yayilgan, S. Y., Yamin, M. M., Ali, S., & Abomhara, M. (2024). Advancing fake news detection: hybrid deep learning with fastText and explainable AI. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3381038
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks' optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872. https://doi.org/10.1016/j.future.2019.02.028
Khanam, Z., Alwasel, B. N., Sirafi, H., & Rashid, M. (2021). Fake news detection using machine learning approaches IOP Conference Series: Materials Science and Engineering, https://doi.org/10.1088/1757-899X/1099/1/012040 https://doi.org/10.1088/1757-899X/1099/1/012040
Kishwar, A., & Zafar, A. (2023). Fake news detection on Pakistani news using machine learning and deep learning. Expert Systems with Applications, 211, 118558. https://doi.org/10.1016/j.eswa.2022.118558
Madani, M., Motameni, H., & Roshani, R. (2024). Fake news detection using feature extraction, natural language processing, curriculum learning, and deep learning. International Journal of Information Technology and Decision Making, 23(3), 1063-1098. https://doi.org/10.1142/S0219622023500347
Mahmud, T., Hasan, I., Aziz, M. T., Rahman, T., Hossain, M. S., & Andersson, K. (2024). Enhanced fake news detection through the fusion of deep learning and repeat vector representations 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), https://doi.org/10.1109/IDCIoT59759.2024.10467839 https://doi.org/10.1109/IDCIoT59759.2024.10467839
Merryton, A. R., & Augasta, M. G. (2024). An attribute-wise attention model with BiLSTM for an efficient fake news detection. Multimedia Tools and Applications, 83(13), 38109-38126. https://doi.org/10.1007/s11042-023-16824-6
Raza, S., Paulen-Patterson, D., & Ding, C. (2025). Fake news detection: comparative evaluation of BERT-like models and large language models with generative AI-annotated data. Knowledge and Information Systems, 1-26. https://doi.org/10.1007/s10115-024-02321-1
Rustam, F., Jurcut, A. D., Alfarhood, S., Safran, M., & Ashraf, I. (2024). Fake news detection using enhanced features through text to image transformation with customized models. Discover Computing, 27(1), 1-26. https://doi.org/10.1007/s10791-024-09490-1
Vereshchaka, A., Cosimini, S., & Dong, W. (2020). Analyzing and distinguishing fake and real news to mitigate the problem of disinformation. Computational and Mathematical Organization Theory, 1-15. https://doi.org/10.1007/s10588-020-09307-8
Wang, X., Meng, J., Zhao, D., Meng, X., & Sun, H. (2025). Fake news detection based on multi-modal domain adaptation. Neural Computing and Applications, 1-13. https://doi.org/10.1007/s00521-024-10896-7
Zhang, Q., Guo, Z., Zhu, Y., Vijayakumar, P., Castiglione, A., & Gupta, B. B. (2023). A deep learning-based fast fake news detection model for cyber-physical social services. Pattern Recognition Letters, 168, 31-38. https://doi.org/10.1016/j.patrec.2023.02.026
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Copyright (c) 2026 Kareem Awad Dawood (Author); Golnaz Aghaee Ghazvini (Corresponding author); Ali Albu-Rghaif, Fariba Majidi (Author)
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