Shahrood University of Technology Journal of AI and Data Mining 2322-5211 14 2 2026 04 01 GAN-Based Anomaly Detection in Social Networks Text Data Using Lasso and Ridge Regression Models 155 167 3722 10.22044/jadm.2025.16297.2753 EN Abolfazl Adressi Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran َAmirhossein Amiri Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran Journal Article 2025 06 02 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. https://jad.shahroodut.ac.ir/article_3722_e75c2b76f25872700a5d2f0a91ba98ec.pdf