Fraudulent account detection in social media using hybrid deep transformer model and hyperparameter optimization
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| Publicado en: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 38447-38470 |
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| 024 | 7 | |a 10.1038/s41598-025-24326-8 |2 doi | |
| 035 | |a 3268295892 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 100 | 1 | |a Shukla, Prashant Kumar |u Department of Computer Science and Engineering & Deputy Dean Research, Amity School of Engineering and Technology (ASET), Amity University Mumbai, 410206, Mumbai, Maharashtra, India (ROR: https://ror.org/02n9z0v62) (GRID: grid.444644.2) (ISNI: 0000 0004 1805 0217) | |
| 245 | 1 | |a Fraudulent account detection in social media using hybrid deep transformer model and hyperparameter optimization | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The high rate of social media development has triggered a high rate of fake accounts, which are a great risk to the privacy of users and the integrity of the platform. These malicious accounts are hard to detect because user activity data is highly imbalanced, dimensional, and sequential. The emergence of fake profiles on social media endangers the privacy and trust of social media users. It is difficult to detect such accounts because of high-dimensional, highly sequential, and imbalanced user behavior data. Current techniques tend to miss out on the complicated activity patterns or even overfit, which is why a strong, scalable, and precise model of social media fraud detection is required. This study suggests a new deep learning architecture that entails a Temporal Convolutional Network (TCN) with Generative Adversarial Network (GAN)-based data augmentation to generate minority classes, and Autoencoder-based feature extraction to reduce dimensionality. The Seagull Optimization Algorithm (SOA), which is a metaheuristic algorithm, is used to optimize hyperparameters by balancing efficiency and speed of convergence in global search. The framework is tested on benchmark datasets (Cresci-2017 and TwiBot-22) and compared to the state-of-the-art models. It has been shown in experiments that the suggested TCN-GAN-SOA framework performs better, with ROC-AUC scores of 0.96 on Cresci-2017 and 0.95 on TwiBot-22, and a higher precision-recall value and better F1-scores. In addition, computational efficiency can be verified by the runtime analysis; case studies prove the framework’s strength when handling various situations of fraudulent behaviors. The given solution offers a scalable, reliable, and accurate methodology of detecting social media fraud based on the combination of sophisticated sequence modeling, realistic data augmentation, and hyperparameter optimization. | |
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Deepfake | ||
| 653 | |a Deep learning | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Algorithms | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Fraud | ||
| 653 | |a Optimization | ||
| 653 | |a Real time | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Social networks | ||
| 653 | |a False information | ||
| 653 | |a Linguistics | ||
| 653 | |a Literature reviews | ||
| 653 | |a Activity patterns | ||
| 653 | |a Social | ||
| 700 | 1 | |a Veerasamy, Bala Dhandayuthapani |u University of Technology and Applied Sciences-Shinas, Shinas, Oman (ROR: https://ror.org/018g8cj68) | |
| 700 | 1 | |a Alduaiji, Noha |u Department of Computer Science, College of Computer and Information Sciences, Majmaah University, 11952, Al Majmaah, Saudi Arabia (ROR: https://ror.org/01mcrnj60) (GRID: grid.449051.d) (ISNI: 0000 0004 0441 5633) | |
| 700 | 1 | |a Addula, Santosh Reddy |u Department of Information Technology, University of the Cumberlands, Williamsburg, KY, United States of America (ROR: https://ror.org/05jz3sn81) (GRID: grid.441548.8) (ISNI: 0000 0000 9229 3752) | |
| 700 | 1 | |a Pandey, Ankur |u Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India (ROR: https://ror.org/040h76494) (ISNI: 0000 0004 4661 2475) | |
| 700 | 1 | |a Shukla, Piyush Kumar |u Department of Computer Science & Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (State Technological University of Madhya Pradesh), 462033, Bhopal, Madhya Pradesh, India (ROR: https://ror.org/03xmje391) (GRID: grid.430236.0) (ISNI: 0000 0000 9264 2828) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 38447-38470 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3268295892/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3268295892/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3268295892/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |