Fake Profile Detection on Social Media

Social media platforms enable communication, news sharing, and online communities, but fake or inauthentic accounts maybe used for spam, impersonation, scams, coordinated manipulation, and misleading content. This paper proposes amachine-learning-based Fake Profile Detection system that evaluates available account and activity features and flags profilesthat may require further review. The planned workflow includes dataset selection, data validation, preprocessing, featureengineering, model training, threshold selection, and evaluation. Candidate methods include Logistic Regression, DecisionTree, Random Forest, and anomaly-detection techniques when suitable. Because the prevalence of fake accounts and thereliability of labels can vary by dataset, accuracy alone is insufficient; precision, recall, F1-score, confusion matrices, andprecision-recall analysis should be considered. This is a proposed academic prototype: no measured accuracy or operationaleffectiveness is claimed. Any real deployment should protect user privacy, limit data collection, reduce bias, and providehuman review before consequential action.