Cross-Domain Fake Reviews Identification Based on Deep Learning Neural Network With Rolling Collaborative Training
DOI:
https://doi.org/10.37385/jaets.v7i2.7944Keywords:
Cross-Domain Classification, Deep Neural Network, Multi-Feature, Rolling Collaborative Training, Identification Fake ReviewsAbstract
Identifying fake reviews in the current digital era has become an interesting study, especially in cross-domain context. This process is based on the limitations of the situation, where not all review domains have labels, and the labeling process takes a long time. In the context of machine learning, cross-domain learning involves learning from data and prediction processes, using data from different domains. Identifying fake reviews, several previous studies have conducted cross-domain, and the results of these studies indicate that there are still several issues in detecting fake reviews. The main problem with cross-domain methods is the difficulty of the model in understanding the differences in characteristics between domains, such as differences in language style, word structure in reviews, and the context present in the reviews. The main problem with the cross-domain method is the difficulty of the model in understanding the differences in characteristics between domains, such as differences in language style, word structure in reviews, and the context present in reviews. Based on these issues, this research adopts an approach to identify fake reviews using the Convolutional Neural Network and Bidirectional Long Short Term Memory models, utilizing the Multi Feature Rolling Collaborative Training (MRCT) algorithm with data from Yelp dan Amazon. The experimental results show that by conducting two scenarios, Scenario-1 provides better performance with an accuracy of 98.59%, while scenario-2 is only capable of providing an accuracy performance of 79.64%. Additionally, by using multi-features, the model experienced a 24.96% improvement in detecting fake reviews across domains. Based on these results, it can be seen that the use of multi-features and rolling collaborative training with the CNN-BiLSTM model works effectively in identifying fake reviews across domains.
Downloads
References
Alsubari, S. N., Deshmukh, S. N., Al-Adhaileh, M. H., Alsaade, F. W., & Aldhyani, T. H. H. (2021). Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets. Applied Bionics and Biomechanics, 2021. https://doi.org/10.1155/2021/5522574
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8
Andresini, G., Iovine, A., Gasbarro, R., Lomolino, M., de Gemmis, M., & Appice, A. (2022). EUPHORIA: A neural multi-view approach to combine content and behavioral features in review spam detection. Journal of Computational Mathematics and Data Science, 3, 100036. https://doi.org/10.1016/j.jcmds.2022.100036
Asudani, D. S., Nagwani, N. K., & Singh, P. (2023). Impact of word embedding models on text analytics in deep learning environment: a review. Artificial Intelligence Review, 56(9), 10345–10425. https://doi.org/10.1007/s10462-023-10419-1
Barushka, A., & Hajek, P. (2019). Review Spam Detection Using Word Embeddings and Deep Neural Networks. IFIP Advances in Information and Communication Technology, 559, 340–350. https://doi.org/10.1007/978-3-030-19823-7_28
Birim, Ş. Ö., Kazancoglu, I., Kumar Mangla, S., Kahraman, A., Kumar, S., & Kazancoglu, Y. (2022). Detecting fake reviews through topic modelling. Journal of Business Research, 149, 884–900. https://doi.org/10.1016/j.jbusres.2022.05.081
Budhi, G. S., Chiong, R., & Wang, Z. (2021). Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features. Multimedia Tools and Applications, 80(9), 13079–13097. https://doi.org/10.1007/s11042-020-10299-5
Cao, N., Ji, S., Chiu, D. K. W., & Gong, M. (2022). A deceptive reviews detection model: Separated training of multi-feature learning and classification. Expert Systems with Applications, 187. https://doi.org/10.1016/j.eswa.2021.115977
Chen, J., Zhang, T., Yan, Z., Zheng, Z., Zhang, W., & Zhang, J. (2025). Attention-based BiLSTM with positional embeddings for fake review detection. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01130-9
Deshai, N., & Bhaskara Rao, B. (2023). Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews. Soft Computing, 27(16), 11357–11378. https://doi.org/10.1007/s00500-023-08507-z
Duma, R. A., Niu, Z., Nyamawe, A. S., Tchaye-Kondi, J., & Yusuf, A. A. (2023). A Deep Hybrid Model for fake review detection by jointly leveraging review text, overall ratings, and aspect ratings. Soft Computing, 27(10), 6281–6296. https://doi.org/10.1007/s00500-023-07897-4
Elmogy, A. M., Tariq, U., Ibrahim, A., & Mohammed, A. (2021). Fake Reviews Detection using Supervised Machine Learning. International Journal of Advanced Computer Science and Applications, 12(1), 601–606. https://doi.org/10.14569/IJACSA.2021.0120169
Fathima Beevi, P. S., Abdul Gafur, M., & Haroon, R. P. (2023). Identification of Fake Reviews: A Review. Proceedings - 2023 International Conference on Innovations in Engineering and Technology, ICIET 2023. https://doi.org/10.1109/ICIET57285.2023.10220907
Gupta, R., Jindal, V., & Kashyap, I. (2024). Recent state-of-the-art of fake review detection: a comprehensive review. In Knowledge Engineering Review (Vol. 39). Cambridge University Press. https://doi.org/10.1017/S0269888924000067
Hajek, P., Barushka, A., & Munk, M. (2020). Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Computing and Applications, 32(23), 17259–17274. https://doi.org/10.1007/s00521-020-04757-2
He, L., Wang, X., Chen, H., & Xu, G. (2022). Online Spam Review Detection: A Survey of Literature. Human-Centric Intelligent Systems, 2(1–2), 14–30. https://doi.org/10.1007/s44230-022-00001-3
Hussain, N., Turab Mirza, H., Hussain, I., Iqbal, F., & Memon, I. (2020). Spam Review Detection Using the Linguistic and Spammer Behavioral Methods. IEEE Access, 8, 53801–53816. https://doi.org/10.1109/ACCESS.2020.2979226
Lainder, A. D., & Wolfinger, R. D. (2022). Forecasting with gradient boosted trees: augmentation, tuning, and cross-validation strategies: Winning solution to the M5 Uncertainty competition. International Journal of Forecasting, 38(4), 1426–1433. https://doi.org/10.1016/j.ijforecast.2021.12.003
Le, T. K. H., Li, Y. Z., & Li, S. T. (2022). Do Reviewers’ Words and Behaviors Help Detect Fake Online Reviews and Spammers? Evidence From a Hierarchical Model. In IEEE Access (Vol. 10, pp. 42167–42183). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3167511
Liu, J., Quan, P., & Zhang, W. (2024). A Study on Fake Review Detection Based on RoBERTa and Behavioral Features. Procedia Computer Science, 242, 1323–1330. https://doi.org/10.1016/j.procs.2024.08.131
Liu, Y., Wang, L., Shi, T., & Li, J. (2022). Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM. Information Systems, 103. https://doi.org/10.1016/j.is.2021.101865
Manaskasemsak, B., Tantisuwankul, J., & Rungsawang, A. (2021). Fake review and reviewer detection through behavioral graph partitioning integrating deep neural network. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-05948-1
Martens, D., & Maalej, W. (2019). Towards understanding and detecting fake reviews in app stores. Empirical Software Engineering, 24(6), 3316–3355. https://doi.org/10.1007/s10664-019-09706-9
Mewada, A., & Dewang, R. K. (2023). A comprehensive survey of various methods in opinion spam detection. Multimedia Tools and Applications, 82(9), 13199–13239. https://doi.org/10.1007/s11042-022-13702-5
Mohawesh, R., Bany Salameh, H., Jararweh, Y., Alkhalaileh, M., & Maqsood, S. (2024). Fake review detection using transformer-based enhanced LSTM and RoBERTa. International Journal of Cognitive Computing in Engineering, 5, 250–258. https://doi.org/10.1016/j.ijcce.2024.06.001
Mohawesh, R., Xu, S., Springer, M., Jararweh, Y., Al-Hawawreh, M., & Maqsood, S. (2023). An explainable ensemble of multi-view deep learning model for fake review detection. Journal of King Saud University - Computer and Information Sciences, 35(8). https://doi.org/10.1016/j.jksuci.2023.101644
Mohawesh, R., Xu, S., Tran, S. N., Ollington, R., Springer, M., Jararweh, Y., & Maqsood, S. (2021). Fake Reviews Detection: A Survey. In IEEE Access (Vol. 9, pp. 65771–65802). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3075573
Pal, K., & Patel, B. V. (2020). Data Classification with k-fold Cross Validation and Holdout Accuracy Estimation Methods with 5 Different Machine Learning Techniques. Proceedings of the 4th International Conference on Computing Methodologies and Communication, ICCMC 2020, 83–87. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00016
Qayyum, H., Ali, F., Nawaz, M., & Nazir, T. (2023). FRD-LSTM: a novel technique for fake reviews detection using DCWR with the Bi-LSTM method. Multimedia Tools and Applications, 82(20), 31505–31519. https://doi.org/10.1007/s11042-023-15098-2
Raheem, M., & Chong, Y. C. (2024). E-Commerce Fake Reviews Detection Using LSTM with Word2Vec Embedding. Journal of Computing and Information Technology, 32(2), 65–80. https://doi.org/10.20532/cit.2024.1005803
Rayana, S., & Akoglu, L. (2015). Collective opinion spam detection: Bridging review networks and metadata. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015-August, 985–994. https://doi.org/10.1145/2783258.2783370
Ren, G., Wang, H., & Yang, Y. (2025). Cross-domain Fake Review Detection Based on Deep Learning MultiLevel Generic Features Extraction Fusion. Informatica (Slovenia), 49(18), 99–110. https://doi.org/10.31449/inf.v49i18.7071
Saumya, S., & Singh, J. P. (2018). Detection of spam reviews: a sentiment analysis approach. CSI Transactions on ICT, 6(2), 137–148. https://doi.org/10.1007/s40012-018-0193-0
Shinde, S. A., Pawar, R. R., Jagtap, A. A., Tambewagh, P. A., Rajput, P. U., Mali, M. K., Kale, S. D., & Mulik, S. V. (2023). Deceptive opinion spam detection using bidirectional long short-term memory with capsule neural network. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-17348-9
Singhal, R., & Kashef, R. (2023). A Weighted Stacking Ensemble Model With Sampling for Fake Reviews Detection. IEEE Transactions on Computational Social Systems. https://doi.org/10.1109/TCSS.2023.3268548
Tang, X., Qian, T., & You, Z. (2020). Generating behavior features for cold-start spam review detection with adversarial learning. Information Sciences, 526, 274–288. https://doi.org/10.1016/j.ins.2020.03.063
Wang, J., Kan, H., Meng, F., Mu, Q., Shi, G., & Xiao, X. (2020). Fake review detection based on multiple feature fusion and rolling collaborative training. IEEE Access, 8, 182625–182639. https://doi.org/10.1109/ACCESS.2020.3028588
Wei, C. S., Hsu, P. Y., Huang, C. W., Cheng, M. S., & Prassida, G. F. (2020). Devising a Cross-Domain Model to Detect Fake Review Comments. Communications in Computer and Information Science, 1287, 714–725. https://doi.org/10.1007/978-3-030-63119-2_58
Xiang, L., You, H., Guo, G., & Li, Q. (2023). Deep feature fusion for cold-start spam review detection. Journal of Supercomputing, 79(1), 419–434. https://doi.org/10.1007/s11227-022-04685-z
Zhang, D., Li, W., Niu, B., & Wu, C. (2023). A deep learning approach for detecting fake reviewers: Exploiting reviewing behavior and textual information. Decision Support Systems, 166. https://doi.org/10.1016/j.dss.2022.113911
CITEDNESS IN SCOPUS
CITEDNESS IN WOS
