Intelligent Detection of Fake Profiles on Social Media Using Machine Learning

Social networking platforms play a vital role in global communication, but they are increasingly vulnerable to security threats due to the presence of fake profiles. Fraudulent accounts are often created for misinformation, cyber fraud, identity theft, cyberbullying, and unauthorized data harvesting, compromising user privacy and damaging the credibility of social media platforms. While existing security systems, such as Facebook's Immune System (FIS), attempt to detect fake accounts, they struggle against sophisticated fraudulent profiles. Traditional detection methods primarily rely on static user data, making them less effective. To improve accuracy and efficiency, this study proposes an advanced machine learning (ML) and natural language processing (NLP)-based approach for fake account detection. The system analyzes both static and dynamic behavioral patterns to distinguish between real and fake accounts. NLP techniques, including tokenization, stemming, and stop-word removal, are applied to examine user-generated text, identifying inconsistencies and unnatural patterns commonly found in fake profiles. The study utilizes datasets from social media platforms like Instagram for training and evaluation. Performance is measured using metrics such as the confusion matrix, correlation heatmap, and classification reports. Results indicate that ML and NLP techniques significantly enhance fake profile detection accuracy compared to traditional methods. By leveraging AI-driven solutions, the system strengthens social media security, prevents misinformation, and protects users from fraudulent activities. Future work can focus on deep learning techniques, dataset expansion, and real-time detection to further improve accuracy and adaptability in fake profile detection.