Document Type : Original/Review Paper
Authors
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
10.22044/jadm.2025.16297.2753
Abstract
Identifying and classifying anomalies in textual data from social networks is challenging due to the linguistic complexity and diverse user expressions. While deep learning and machine learning techniques offer promise in tackling this problem, their effectiveness is limited by insufficient data. The effect of Generative Adversarial Networks (GANs) on anomaly detection and Classification is assessed in this paper, along with their relevance for generating synthetic text data. Combining synthetic and real data enhances classification accuracy, especially in settings of limited data. In this paper, Lasso and Ridge regression techniques are used for anomaly detection and classification. Experimental results reveal the superior performance of the proposed model in identifying and classifying anomalies under new datasets generated by GAN. By combining statistical methods with generative techniques, the solution becomes not only more interpretable and scalable but also better suited for advanced text analysis in fast-changing environments like social media platforms.
Keywords
- Social Networks
- Anomaly Classification
- Generative Adversarial Networks
- Machine Learning
- Lasso and Ridge Regression
Main Subjects
[1] C. C. Aggarwal, "Outlier ensembles: position paper," ACM SIGKDD Explorations Newsletter, vol. 14, no. 2, pp. 49–58, 2013.
[2] V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey," ACM Computing Surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009.
[3] V. Hodge and J. Austin, "A survey of outlier detection methodologies," Artificial Intelligence Review, vol. 22, pp. 85–126, 2004.
[4] J. Mao, Y. Yang, and T. Zhang, "Empirical analysis of attribute inference techniques in online social network," IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 881–893, 2020.
[5] D. Tiwari et al., "A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques," Artificial Intelligence Review, vol. 56, no. 11, pp. 13407–13461, 2023.
[6] C. Ahmed, A. ElKorany, and E. ElSayed, "Prediction of customer's perception in social networks by integrating sentiment analysis and machine learning," Journal of Intelligent Information Systems, vol. 60, no. 3, pp. 829–851, 2023.
[7] S. Shan, Y. Yang, and C. Li, "Which Receives More Attention, Online Review Sentiment or Online Review Rating? Spillover Effect Analysis from JD.com," Behavioral Sciences, vol. 14, no. 9, p. 823, 2024.
[8] M. M. Breunig, H. P. Kriegel, R. T. Ng, and J. Sander, "LOF: identifying density-based local outliers," in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2000, pp. 93–104.
[9] S. Wette and F. Heinrichs, "OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data," arXiv preprint, arXiv:2409.09742, 2024. [Online]. Available: https://arxiv.org/abs/2409.09742. [Accessed: Jun. 1, 2024].
[10] R. Domingues, M. Filippone, P. Michiardi, and J. Zouaoui, "A comparative evaluation of outlier detection algorithms: Experiments and analyses," Pattern Recognition, vol. 74, pp. 406–421, 2018.
[11] E. Manzoor, H. Lamba, and L. Akoglu, "xstream: Outlier detection in feature-evolving data streams," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM, 2018, pp. 1963–1972.
[12] É. Courtois, P. Tubert-Bitter, and I. Ahmed, "New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection," BMC Medical Research Methodology, vol. 21, pp. 1–17, 2021.
[13] E. S. Groenewald et al., "Real-Time Anomaly Detection in Industrial Systems Using Stream Processing and Online Machine Learning," in International Conference on Deep Learning and Visual Artificial Intelligence. Singapore: Springer, 2024, pp. 513–529.
[14] D. P. Senapati et al., "DG-GAN: A Deep Neural Network for Real-World Anomaly Detection in Surveillance Videos," in International Conference on Advanced Network Technologies and Intelligent Computing. Cham: Springer, 2023, pp. 93–106.
[15] Y. Su et al., "Robust anomaly detection for multivariate time series through stochastic recurrent neural network," in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM, 2019, pp. 2828–2837.
[16] P. Perera, R. Nallapati, and B. Xiang, "Ocgan: One-class novelty detection using gans with constrained latent representations," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE, 2019, pp. 2898–2906.
[17] T. Schlegl et al., "f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks," Medical Image Analysis, vol. 54, pp. 30–44, 2019.
[18] S. Akcay, A. Atapour-Abarghouei, and T. P. Breckon, "Ganomaly: Semi-supervised anomaly detection via adversarial training," in Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Revised Selected Papers, Part III. Cham: Springer, 2019, pp. 622–637.
[19] F. H. K. D. S. Tanaka and C. Aranha, "Data augmentation using GANs," arXiv preprint, arXiv:1904.09135, 2019. [Online]. Available: https://arxiv.org/abs/1904.09135. [Accessed: Oct. 10, 2023].
[20] S. Chen, M. Peng, H. Xiong, and S. Wu, "An anomaly detection method based on Lasso," Cluster Computing, vol. 22, pp. 5407–5419, 2019.
[21] N. K. Singh and K. Raza, "Medical image generation using generative adversarial networks: A review," in Health Informatics: A Computational Perspective in Healthcare. Singapore: Springer, 2019, pp. 77–96.
[22] M. S. Meor Yahaya and J. Teo, "Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience," Frontiers in Applied Mathematics and Statistics, vol. 9, p. 1162760, 2023.
[23] J. Naseri, H. Hasanpour, and A. Ghanbari Sorkhi, "Accelerating Legislation Processes through Semantic Similarity Analysis with BERT-based Deep Learning," International Journal of Engineering, Transactions C: Aspects, vol. 37, no. 6, pp. 1050–1058, 2024.
[24] A. Yavari and H. Hasanpour, "Election Prediction Based on Messages Feature Analysis in Twitter Social Network," International Journal of Engineering, Transactions C: Aspects, vol. 36, no. 6, pp. 1179–1184, 2023.
[25] M. Ahangari and A. Sebti, "A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User's Opinions," International Journal of Engineering, Transactions C: Aspects, vol. 36, no. 3, pp. 573–584, 2023.
[26] Hastie, T., Tibshirani, R., & Friedman, J., The elements of statistical learning. Springer, 2009.
[27] A. Abhari, "Towards New Simulation Models for DL-Based Video Streaming in Edge Networks, " Proceedings of the 2024 Winter Simulation Conference (WSC). Los Alamitos, CA: IEEE, pp. 656–665, 2024.
[28] A. Mohammadi Gohar, K. Rahbar, B. Minaei-Bidgoli, and Z. Beheshtifard, "Study on Generative Adversarial Network in Discrete Data: A Survey," Journal of Artificial Intelligence and Data Mining, vol. 12, no. 4, pp. 567–581, 2024.
)