FINDING FAKE SOCIAL MEDIA ACCOUNT USING MACHINE LEARNING

Abstract: The widespread use of social media has resulted in a surge of fake accounts, posing seriousrisks to individuals, organizations, and society at large. Identifying fake accounts effectively isessential to preserving the integrity and credibility of social media platforms. This study introduces amachine learning-based approach to detect fake social media accounts.We employed five machinelearning algorithms—Support Vector Machines (SVM), K-Nearest Neighbors (KNN), RandomForest, Logistic Regression, and Artificial Neural Networks (ANN)—to classify accounts as fake orgenuine. The dataset used in this study consisted of features extracted from social media profiles, suchas user behavior, profile details, and network characteristics. Experimental results revealed that theANN algorithm outperformed the others, achieving a high accuracy of 95.6% in detecting fakeaccounts.The proposed approach offers significant benefits for social media platforms by enablingmore efficient detection and prevention of fake accounts. Furthermore, the findings of this study canguide the development of advanced fake account detection systems, contributing to a safer and morereliable online environment. Keywords: Fake account detection, Support Vector Machines (SVM), K-Nearest Neighbors (KNN),Random Forest, Logistic Regression, Artificial Neural Networks (ANN), classification, behavioranalysis.