Fake Profile Detection on Social Media Using Machine Learning

Social media supports communication, learning, entertainment, and business, but fake or misleading accounts may spreadspam, impersonate people, manipulate engagement, or direct users to fraudulent content. Manual identification is difficult atscale. This paper proposes a supervised machine-learning workflow to classify profiles as potentially genuine or potentiallyfake using available profile features. The planned pipeline includes a labelled dataset, data cleaning, feature preparation, traintest splitting, and a Random Forest classifier. Candidate features include profile-picture availability, biography and usernamecharacteristics, follower/following counts, post count, and account age when lawfully available. Performance will be assessedwith precision, recall, F1-score, accuracy, and a confusion matrix. False accusations and missed fake accounts are bothimportant risks. This is a research plan, not a completed experiment; no project-specific performance score is clemed .