A Comparative Analysis of Ensemble Learning Methods on Social Media Account Detection

Year 2023, , 87 - 105, 31.08.2023

Merve Varol Arısoy , Tuğba Tunç Abubakar

https://doi.org/10.30931/jetas.1325483

Cited By: 1

Abstract

Today, social media platforms usage and benefiting rate from these environments are increasing. This rapid spread of social media has also allowed the emergence of fake accounts. Fake accounts are generally created to implement malicious activities through another user account or to spread incorrect information. To prevent the detriment that this situation may cause to real individuals, an effective fake account detection was carried out by using ensemble learning methods (Bagging, Boosting, Stacking, Voting and Blending) in this study. These methods were combined with various machine learning algorithms to measure their effectiveness in detecting fake accounts. The experimental results suggested that Bagging technique attained an accuracy level of 90.441%, Stacking technique 89.706%, Voting technique 88.971% and the Blending technique attained 88.235% in the test phase. While for the Boosting methods, XGboost technique attained accuracy level of 86.765%, whereas the AdaBoost outperformed it with an accuracy level of 91.912% in the test phase. The extant results demonstrates that ensemble learning methods combined with machine learning algorithms are efficient in detecting fake social media accounts. It is considered that additional studies with larger datasets alongside the usage of different ensemble methods can further improve the accuracy of the detection process.

Keywords

Ensemble learning, social media fake account detection, bagging, blending, boosting, stacking, voting

Supporting Institution

No funding

References

• [1] Van Der Walt, E., Eloff, J., "Using machine learning to detect fake identities: bots vs humans", IEEE Access 6 (2018) : 6540-6549.
• [2] Ali, A. K., Abdullah, A. M., "Fake accounts detection on social media using stack ensemble system", International Journal of Electrical and Computer Engineering (IJECE) 12(3) (2022) : 3013-3022.
• [3] Al-Qurishi, M., Al-Rakhami, M., Alamri, A., Alrubaian, M., Rahman, S. M. M., Hossain, M. S., "Sybil defense techniques in online social networks: a survey", IEEE Access 5 (2017) : 1200-1219.
• [4] Wang, G., Wilson, C., Zhao, X., Zhu, Y., Mohanlal, M., Zheng, H., Zhao, B. Y., "Serf and turf: crowdturfing for fun and profit", In Proceedings of the 21st international conference on World Wide Web (2012) : 679-688.
• [5] Erşahin, B., Aktaş, Ö., Kılınç, D., Akyol, C., "Twitter fake account detection", In 2017 International Conference on Computer Science and Engineering (UBMK) (2017): 388-392.
• [6] Adewole, K. S., Han, T., Wu, W., Song, H., Sangaiah, A. K., "Twitter spam account detection based on clustering and classification methods", The Journal of Supercomputing 76 (2020) : 4802-4837.
• [7] Gayathri, A., Radhika, S., Jayalakshmi, S. L., "Detecting fake accounts in media application using machine learning", International Journal of Advanced Networking and Applications (2019) : 234-237.
• [8] Mulamba, D., Ray, I., Ray, I., "Sybilradar: A graph-structure based framework for sybil detection in on-line social networks", In ICT Systems Security and Privacy Protection: 31st IFIP TC 11 International Conference 31 (2016) : 179-193.
• [9] Stein, T., Chen, E., Mangla, K., "Facebook immune system", In Proceedings of the 4th workshop on social network systems (2011) : 1-8.
• [10] Abokhodair, N., Yoo, D., McDonald, D. W., "Dissecting a social botnet: Growth, content and influence in Twitter", In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (2015) : 839-851.
• [11] Cao, Q., Sirivianos, M., Yang, X., Pregueiro, T., "Aiding the detection of fake accounts in large scale social online services", In 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12) (2012) : 197-210.
• [12] Akyon, F. C., Kalfaoglu, M. E., "Instagram fake and automated account detection", In 2019 Innovations in intelligent systems and applications conference (ASYU) (2019) : 1-7.
• [13] Kalirane, M., "Ensemble Learning Methods: Bagging, Boosting and Stacking", Available from: https://www.analyticsvidhya.com/blog/2023/01/ensemble-learning-methods-bagging-boosting-and-stacking/ (Accessed June, 2023).
• [14] Kim, C., "Ensemble Learning - Voting and Bagging with Python", Available from: https://medium.com/@chyun55555/ensemble-learning-voting-and-bagging-with-python-40de683b8ff0 (Accessed June, 2023).
• [15] Python kitchen, "Blending Algorithms in Machine Learning", Available from: https://www.pythonkitchen.com/blending-algorithms-in-machine-learning/ (Accessed June, 2023).
• [16] Kaggle,https://www.kaggle.com/code/iamamir/fake-social-media-account-detection/input (Accessed June, 2023).
• [17] Kadam, V. J., Jadhav, S. M., Vijayakumar, K., "Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression", Journal of medical systems 43(8) (2019) : 263.