Finding Fake Social Media Account Using Machine Learning
Year: 2025
Type: article
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.
Authors: Rohini Ashok Gamane, Dr.Vaibhav Dabhade
Institution: Savitribai Phule Pune University
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949377103
FWCI:
Cites:
Cited by:
Related to: