Spammer Detection and Fake User Identification in Social Networks

Online Social Networks (OSNs) have evolved from simple communication platforms into essential tools for public discourse,informationsharing,anddigital engagement. With their rapid expansion, these networks have become breeding grounds for malicious actors who create fake accounts and spammers to spread misinformation,manipulatepublicopinion, and disrupt authentic communication. Traditional detection methods such as manual moderation and rule-based filters have proven ineffective against sophisticated, automated spam behavior. Thisresearchproposesamachinelearning- based framework for detecting spammers and fake users by analyzing behavioral features such as tweet frequency, follower- following ratios, hashtag density, and temporalactivitypatterns.Dataiscollected via APIs and web scraping tools, preprocessed, and used to train classification models including Logistic Regression, SVM, and kNN. Principal Component Analysis (PCA) is applied for dimensionality reduction, and model performance is evaluated using accuracy, precision,recall,F1-score,andROC-AUC metrics. The experimental results demonstrate high classification accuracy and robustness, validating the system’s potential for realtime integration into social media monitoring tools. This framework offers a scalable, automated, and intelligent solutiontoenhancingtrustandauthenticity in online social environments