Social Media Fake Account Detection Using Machine Learning.

The rise of social media platforms has led to an increase in the number of fake accounts, posing a significant challenge in maintaining user trust and platform integrity. To address this, machine learning algorithms have been employed for the detection and identification of fake accounts. In this project, we explore several supervised machine learning techniques, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Artificial Neural Networks (ANN). These algorithms analyze account behaviors, interactions, and other user-specific features to classify accounts as legitimate or fake. By leveraging diverse datasets and evaluating various feature sets, the models aim to improve the accuracy of fake account identification, offering a robust solution for enhancing social media safety. Each algorithm brings unique strengths to the identification process. SVM excels in handling high-dimensional data, while KNN is useful for local proximity-based classification. Logistic Regression offers a probabilistic framework that is simple and interpretable, whereas ANN, with its multi-layered structure, enables complex pattern recognition. Our comparative analysis of these techniques highlights their effectiveness and trade-offs, providing a comprehensive approach to detecting fake accounts and aiding in developing more secure social media platforms.