Deep Learning Technique to Detect Fake Accounts on Social Media

Recognizing fraudulent activity on social media has grown in importance in the digital realm. Facebook has 11% of monthly active users who are duplicates (about 150 million as of Q32023) and 5% who are classified as "false" or "undesirable" (around 137 million) in the face of this growing difficulty. Discussions on Instagram indicated a 13% prevalence, or 270 million phony accounts. This study presents new methods and techniques for identifying and thwarting fraudulent behavior onpopular social media platforms. By using cutting-edge machine learning techniques and data analytics, the research focuses on anomaly detection, pattern recognition, and user behavior analysis to distinguish real user interactions from fraudulentones. Fraudulent activity on social networking sites poses a serious threat to users, businesses, and online communities. Using the Pandas library, this article presents a strong fraud detection system that makes use of the synergies between Random Forest, NLTK(Natural Language Toolkit), ML (Machine Learning) and recurrent neural networks approaches. Combining random forest, with ensemble learning enhances and deep learning fraud detection accuracy and effectiveness. A complete model that can identify fraudulent activity.