PREDICTION OF FAKE INSTAGRAM PROFILE USING MACHINE LEARNING

Abstract

The rapid growth of social media platforms, particularly Instagram, has led to an increase in fake profiles that are often used for malicious activities such as spamming, phishing, and spreading misinformation. Detecting such fake accounts has become a critical challenge to ensure user safety and platform integrity. This project focuses on the development of a machine learning-based system to predict and identify fake Instagram profiles. These days, most people's daily routines include checking some kind of social media site. Multiple users sign up for social media accounts daily, and those users engage in real-time, location-independent communication with one another. While social media sites do have many positive uses, they also pose risks to users' privacy and the information they provide. In order to determine whether people are actively promoting danger on social media, it is necessary to categorize their accounts. We can distinguish between real and phony social media accounts using the categorization. In the past, there were a variety of categorization algorithms used to identify phony accounts on social media. Nevertheless, the social media platforms' ability to identify false profiles needs improvement. In order to increase the reliability of identifying false profiles, we provide methods based on machine learning and natural language processing (NLP) in this article. The Naïve Bayes method and the Support Vector Machine (SVM) are both at our disposal.