Comparative Analysis of Fake Account Detection Using Machine Learning Algorithms

With the rise of Social Media platforms and new applications, the Rapid Expansion of fake accounts has become an important concern, posing threats to security, privacy and trust-worthiness. In response, this research explores the application of machine learning techniques for the detection and reduction of fake accounts. By utilizing datasets containing, user behavior patterns, network characteristics and various parameters, we explore the efficiency of various machine learning algorithms in selective genuine users from false ones. This study includes attribute manipulation, model training and evaluation techniques made for the unique challenges of fake account detection. We examine the effects of different features, such as interaction measures, language patterns and network structures on the output of detection models. Systematic testing and validation reveal effective methods for identifying fake accounts, enhancing information security and user trust on online platforms.