Fraudulent account detection in social media using hybrid deep transformer model and hyperparameter optimization Article Swipe
Prashant Kumar Shukla
,
Bala Dhandayuthapani Veerasamy
,
Noha Alduaiji
,
Santosh Reddy Addula
,
Ankur Pandey
,
Noha Alduaiji
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-24326-8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-24326-8
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.
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- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-24326-8
- https://www.nature.com/articles/s41598-025-24326-8.pdf
- OA Status
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- Cited By
- 1
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- 34
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https://openalex.org/W4415816521Canonical identifier for this work in OpenAlex
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https://doi.org/10.1038/s41598-025-24326-8Digital Object Identifier
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Fraudulent account detection in social media using hybrid deep transformer model and hyperparameter optimizationWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-11-03Full publication date if available
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Prashant Kumar Shukla, Bala Dhandayuthapani Veerasamy, Noha Alduaiji, Santosh Reddy Addula, Ankur Pandey, Noha AlduaijiList of authors in order
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https://doi.org/10.1038/s41598-025-24326-8Publisher landing page
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https://www.nature.com/articles/s41598-025-24326-8.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-24326-8.pdfDirect OA link when available
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1Total citation count in OpenAlex
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| abstract_inverted_index.global | 168 |
| abstract_inverted_index.higher | 212 |
| abstract_inverted_index.highly | 43, 75 |
| abstract_inverted_index.offers | 245 |
| abstract_inverted_index.reduce | 143 |
| abstract_inverted_index.scores | 201 |
| abstract_inverted_index.social | 4, 54, 62, 106, 254 |
| abstract_inverted_index.tested | 173 |
| abstract_inverted_index.users. | 64 |
| abstract_inverted_index.Current | 82 |
| abstract_inverted_index.Network | 125, 130 |
| abstract_inverted_index.ROC-AUC | 200 |
| abstract_inverted_index.Seagull | 146 |
| abstract_inverted_index.because | 38, 72 |
| abstract_inverted_index.better, | 198 |
| abstract_inverted_index.entails | 121 |
| abstract_inverted_index.feature | 140 |
| abstract_inverted_index.models. | 185 |
| abstract_inverted_index.precise | 103 |
| abstract_inverted_index.privacy | 22, 58 |
| abstract_inverted_index.runtime | 227 |
| abstract_inverted_index.search. | 169 |
| abstract_inverted_index.strong, | 100 |
| abstract_inverted_index.studies | 230 |
| abstract_inverted_index.various | 237 |
| abstract_inverted_index.Temporal | 123 |
| abstract_inverted_index.accounts | 33, 71 |
| abstract_inverted_index.accurate | 250 |
| abstract_inverted_index.activity | 40, 91 |
| abstract_inverted_index.behavior | 80 |
| abstract_inverted_index.classes, | 137 |
| abstract_inverted_index.compared | 181 |
| abstract_inverted_index.datasets | 176 |
| abstract_inverted_index.generate | 135 |
| abstract_inverted_index.handling | 236 |
| abstract_inverted_index.learning | 118 |
| abstract_inverted_index.minority | 136 |
| abstract_inverted_index.optimize | 158 |
| abstract_inverted_index.overfit, | 95 |
| abstract_inverted_index.patterns | 92 |
| abstract_inverted_index.performs | 197 |
| abstract_inverted_index.profiles | 52 |
| abstract_inverted_index.sequence | 263 |
| abstract_inverted_index.solution | 244 |
| abstract_inverted_index.strength | 234 |
| abstract_inverted_index.suggests | 114 |
| abstract_inverted_index.verified | 224 |
| abstract_inverted_index.Algorithm | 148 |
| abstract_inverted_index.accounts, | 14 |
| abstract_inverted_index.addition, | 219 |
| abstract_inverted_index.analysis; | 228 |
| abstract_inverted_index.balancing | 161 |
| abstract_inverted_index.benchmark | 175 |
| abstract_inverted_index.detecting | 253 |
| abstract_inverted_index.detection | 109 |
| abstract_inverted_index.difficult | 67 |
| abstract_inverted_index.emergence | 49 |
| abstract_inverted_index.endangers | 56 |
| abstract_inverted_index.framework | 171, 196 |
| abstract_inverted_index.integrity | 27 |
| abstract_inverted_index.malicious | 32 |
| abstract_inverted_index.modeling, | 264 |
| abstract_inverted_index.platform. | 30 |
| abstract_inverted_index.realistic | 265 |
| abstract_inverted_index.reliable, | 248 |
| abstract_inverted_index.required. | 111 |
| abstract_inverted_index.scalable, | 101, 247 |
| abstract_inverted_index.suggested | 194 |
| abstract_inverted_index.triggered | 8 |
| abstract_inverted_index.F1-scores. | 217 |
| abstract_inverted_index.Generative | 128 |
| abstract_inverted_index.TwiBot-22) | 179 |
| abstract_inverted_index.TwiBot-22, | 209 |
| abstract_inverted_index.algorithm, | 154 |
| abstract_inverted_index.behaviors. | 241 |
| abstract_inverted_index.efficiency | 162, 221 |
| abstract_inverted_index.extraction | 141 |
| abstract_inverted_index.fraudulent | 240 |
| abstract_inverted_index.imbalanced | 78 |
| abstract_inverted_index.situations | 238 |
| abstract_inverted_index.techniques | 83 |
| abstract_inverted_index.(GAN)-based | 131 |
| abstract_inverted_index.Adversarial | 129 |
| abstract_inverted_index.Cresci-2017 | 205 |
| abstract_inverted_index.TCN-GAN-SOA | 195 |
| abstract_inverted_index.combination | 260 |
| abstract_inverted_index.complicated | 90 |
| abstract_inverted_index.convergence | 166 |
| abstract_inverted_index.development | 6 |
| abstract_inverted_index.experiments | 191 |
| abstract_inverted_index.framework's | 233 |
| abstract_inverted_index.imbalanced, | 44 |
| abstract_inverted_index.methodology | 251 |
| abstract_inverted_index.sequential, | 76 |
| abstract_inverted_index.sequential. | 47 |
| abstract_inverted_index.(Cresci-2017 | 177 |
| abstract_inverted_index.Optimization | 147 |
| abstract_inverted_index.architecture | 119 |
| abstract_inverted_index.augmentation | 133 |
| abstract_inverted_index.dimensional, | 45 |
| abstract_inverted_index.Convolutional | 124 |
| abstract_inverted_index.augmentation, | 267 |
| abstract_inverted_index.computational | 220 |
| abstract_inverted_index.metaheuristic | 153 |
| abstract_inverted_index.optimization. | 270 |
| abstract_inverted_index.sophisticated | 262 |
| abstract_inverted_index.hyperparameter | 269 |
| abstract_inverted_index.dimensionality. | 144 |
| abstract_inverted_index.hyperparameters | 159 |
| abstract_inverted_index.precision-recall | 213 |
| abstract_inverted_index.state-of-the-art | 184 |
| abstract_inverted_index.Autoencoder-based | 139 |
| abstract_inverted_index.high-dimensional, | 74 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5021608731 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I73779912 |
| citation_normalized_percentile |