Fake Profile Detection System Using Machine Learning Techniques
Author : B Sasikala and R Gomathi Jayam
Abstract :
The rapid growth of social networking platforms such as Facebook, Instagram, and LinkedIn has significantly increased digital interaction worldwide. However, this expansion has also led to a surge in fake profiles used for scams, phishing, cyberbullying, impersonation, and misinformation. Traditional detection methods rely heavily on manual reporting and rule-based filtering, which are often inefficient and inaccurate. This paper presents a Fake Profile Detection System developed using Machine Learning techniques to automatically classify social media accounts as fake or genuine. The system is implemented using the Flask framework with a Python backend and a web-based frontend interface. Key profile attributes such as follower count, following count, number of posts, and account age are analysed using a trained classification model. The system provides real-time prediction with reasoning and stores analysis results in a database for future reference. Experimental results demonstrate improved detection accuracy and faster response compared to existing manual approaches.
Keywords :
Fake Profile Detection, Machine Learning, Social Media Security, Cyber security, Flask Web Application.
