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Original Article
Fake Profile Detection in Social Media Using Multi-Layer Ensemble Machine Learning with Live API Enrichment and Forensic Analysis
P. Anu Uthayam1
Gowtham raj2
Jayadithya K3
Bharathkumar S4
Devanandhan G5
1 Assistant Professor, Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur,Tamil Nadu, India. 2 3 4 5 UG Scholer, Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu, India.
Published Online: January-April 2026
Pages: 551-557
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501062
References
1. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi, “Fame for sale: Efficient detection of fake Twitter followers,” Decision Support Systems, vol. 80, pp. 56–71, 2015.
2. Z. Yang, C. Wilson, X. Wang, T. Gao, B. Y. Zhao, and Y. Dai, “Uncovering social network sybils in the wild,” ACM Trans. Knowl. Discov. Data, vol. 8, no. 1, pp. 1–29, 2014.
3. S. Kudugunta and E. Ferrara, “Deep neural networks for bot detection,” Information Sciences, vol. 467, pp. 312–322, 2018.
4. S. Feng, H. Wan, N. Wang, J. Li, and M. Luo, “TWIBOT-20: A comprehensive Twitter bot detection benchmark,” in Proc. 30th ACM Int. Conf. Inf. Knowl. Manage., 2021, pp. 4485–4494.
5. F. Giglietto, N. Righetti, L. Rossi, and G. Marino, “It takes a village to manipulate the media: Coordinated link sharing behavior during 2018 and 2019 Italian elections,” Inf., Commun. Soc., vol. 23, no. 6, pp. 867–891, 2020.
6. S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
7. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
8. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2016, pp. 785–794.
9. G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
10. T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A next-generation hyperparameter optimization framework,” in Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2019, pp. 2623– 2631.
2. Z. Yang, C. Wilson, X. Wang, T. Gao, B. Y. Zhao, and Y. Dai, “Uncovering social network sybils in the wild,” ACM Trans. Knowl. Discov. Data, vol. 8, no. 1, pp. 1–29, 2014.
3. S. Kudugunta and E. Ferrara, “Deep neural networks for bot detection,” Information Sciences, vol. 467, pp. 312–322, 2018.
4. S. Feng, H. Wan, N. Wang, J. Li, and M. Luo, “TWIBOT-20: A comprehensive Twitter bot detection benchmark,” in Proc. 30th ACM Int. Conf. Inf. Knowl. Manage., 2021, pp. 4485–4494.
5. F. Giglietto, N. Righetti, L. Rossi, and G. Marino, “It takes a village to manipulate the media: Coordinated link sharing behavior during 2018 and 2019 Italian elections,” Inf., Commun. Soc., vol. 23, no. 6, pp. 867–891, 2020.
6. S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
7. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
8. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2016, pp. 785–794.
9. G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
10. T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A next-generation hyperparameter optimization framework,” in Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2019, pp. 2623– 2631.
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