Spammer Detection and Fake user Identification on Social Networks

The proliferation of spam and fake accounts on Twitter poses significant threats to information integrity, user privacy and the overall trustworthiness of online social networks. Despite substantial advancements in spam detection methodologies, existing approaches face critical limitations including reliance on static features, vulnerability to evolving spammer tactics and inadequate consideration of real-time detection capabilities. This paper presents a novel hybrid framework for real-time Twitter spam detection that integrates ensemble machine learning techniques with dynamic behavioral analysis. The proposed framework combines Decision Tree, Random Forest and Gradient Boosting classifiers within a stacking ensemble architecture, augmented with temporal feature engineering to capture evolving spam patterns. Experimental evaluation on a comprehensive dataset of 500,000 Twitter accounts demonstrates that the proposed framework achieves 98.7% accuracy, outperforming traditional single-classifier approaches by 3-5%. Furthermore, the framework incorporates a privacy-preserving feature extraction mechanism that minimizes access to sensitive user data while maintaining detection efficacy. The results highlight the framework’s robustness against concept drift and its potential for deployment in real-world social media moderation systems.