Detecting Fake Profiles on Social Networks Using Machine Learning and NLP

This study offers a method for spotting phony social network profiles using machine learning and natural language processing (NLP). The model analyzes user behavior and content patterns using supervised learning techniques including Decision Trees, Random Forest, and Gradient Boosting. Sentiment analysis and topic modeling are two NLP approaches that are used to find discrepancies and fraudulent activity. The system can be implemented in hardware by integrating it into ESP modules for edge computing scenarios that require real-time monitoring and detection. The experiment's findings demonstrate a high degree of accuracy in spotting phony profiles, improving social networks' security and dependability.