Detecting Fake Accounts with Machine Learning Techniques: A Case Study on the X Social Network (Formerly Twitter)

Document Type : Original Article

Authors
Mohammad Rajabian 1
Monireh Hosseini 2
1 MSc., Department of Information Technology, Faculty of Industry, K.N. Toosi Universityof Technology, Tehran, Iran
2 Associate Prof., Department of Information Technology, Faculty of Industry, K.N. Toosi Universityof Technology, Tehran, Iran
10.22034/aimj.2025.489949.1616
Abstract
The popularity of online social networks (OSNs) is increasing daily. This has led to malicious individuals exploiting these platforms. Often, these individuals use fake accounts to carry out their malicious actions. Many researchers have focused on identifying fake user accounts on various social networks using machine learning methods. Most previous studies have used feature reduction, selection, or extraction methods to reduce the number of features and increase the speed when using ensemble machine learning methods to accurately identify fake user accounts. In the current research, 67 numerical features were selected to identify fake user accounts, which ensures high classification speed and eliminates the need for feature reduction. Additionally, a new hybrid method has been proposed in which the Support Vector Machine (SVM) algorithm is optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Neural Networks (NN). In the current research, the best average accuracy achieved over one hundred runs of the algorithms was 98.28%, which occurred when combining the SVM algorithm with NN.

Keywords

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  • Receive Date 25 November 2024
  • Revise Date 19 March 2025
  • Accept Date 07 April 2025