Fake Profile Detection using Machine Learning Algorithms

Abstract

Fake profiles resulting from the explosion of social media and internet platforms provide major problems for digital security, privacy, and user confidence since they multiply exponentially. Emerging as a potent method to identify and reduce these false identities is machine learning. Using cutting-edge methods in artificial intelligence, pattern recognition, and data mining, scientists are creating complex models able to differentiate between real and created user profiles. Usually analyzing several features and behavior patterns, these machine learning techniques help to find possible false profiles. Key symptoms are erratic personal information, odd account creation timestamps, scant or generic profile material, aberrant interaction patterns, and statistical irregularities in network connections. Deep learning models—including support vector machines and neural networks—can analyze intricate multi-dimensional data to produce strong categorization systems. These algorithms learn complex distinguishing traits by training on large databases including both real-world and synthetic profiles. Modern research aims to create dynamic and adaptable detection systems that can quickly change with ever advanced methods of profile generation. Machine learning algorithms keep improving their accuracy in spotting and stopping false profile proliferation across digital platforms by combining several data sources, applying ensemble learning techniques, and using advanced feature extraction methods.