Automated Detection of Fake Twitter Accounts Using Ensemble Machine Learning Models

Ahmed Daai Abdulkareem

doi:10.26706/ijceae.7.2.20260312

Abstract

Social media has been increasing in popularity, particularly Twitter, and has been rife with fake and automated accounts used to misinform, scam and influence opinions. These evil accounts can prove to be a threat to the credibility and security of online social networks. So, intelligent and automatic detection systems are required. In this study, an automated framework for fake Twitter accounts detection based on ensemble machine learning models is proposed. The proposed approach is based on analyzing various types of features derived from user profiles, behavioral activities and network-based features. Various classifiers, namely Random Forest, Support Vector Machine and Logistic Regression classifiers are preprocessed and feature engineered and combined using ensemble learning for improved classification. The ensemble model is the combination of the results of several classifiers to get better detection and to reduce the false alarm rate. The evaluation is experimental by using the labeled Twitter datasets and performance of the proposed approach is evaluated using the standard evaluation indices such as accuracy, precision, recall and F1-score. The results demonstrate the reliability of ensemble learning in fake account detection, compared with individual machine learning models.

Keywords

Fake Accounts Twitter Ensemble Learning Machine Learning Social Network Security Bot Detection

References

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[3] W. Dracewicz and M. Sepczuk, "Detecting fake accounts on social media portals—The X portal case study," Electronics, vol. 13, no. 13, Art. no. 2542, 2024.

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[12] H. M. Silva, "The reconfiguration of social bonds in the digital age: Virtual connections vs. face-to-face relationships," Nature Anthropology, vol. 3, no. 1, Art. no. 10003, 2025.

[13] S. Terumalasetti, "Artificial intelligence-based approach to detect malicious users using deep learning and optimization techniques," Multimedia Tools and Applications, vol. 84, no. 8, pp. 3979–4001, 2025.

[14] K.-C. Yang, D. Singh, and F. Menczer, "Characteristics and prevalence of fake social media profiles with AI-generated faces," arXiv preprint, arXiv:2401.02627, 2024.

[15] L. Zhang, "The digital age of religious communication: The shaping and challenges of religious beliefs through social media," Studies on Religion and Philosophy, vol. 1, no. 1, pp. 25–41, 2025.

[16] Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms. Boca Raton, FL, USA: Chapman & Hall/CRC, 2025.