A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification


  • Swetha Chikkasabbenahalli Venkatesh School of Computational Sciences & IT, Garden City University, Bangalore, India
  • Sibi Shaji School of Computational Sciences & IT, Garden City University, Bangalore, India
  • Balasubramanian Meenakshi Sundaram Department of Computer Science & Engineering, New Horizon College of Engineering, Bangalore, India




fake profile, online social networks, stacked ensemble, imbalanced dataset, cost-sensitive learning


Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models.


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How to Cite

Swetha Chikkasabbenahalli Venkatesh, Sibi Shaji, and Balasubramanian Meenakshi Sundaram, “A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification”, Proc. eng. technol. innov., vol. 26, pp. 18–32, Feb. 2024.