SPAMMER DETECTION AND FAKE USER IDENTIFICATION ON SOCIAL NETWORKS

Abstract

The project titled "Spammer Detection and Fake User Identification on Social Networks" aims to explore and implement a novel approach to concealing information within digital images while preserving their visual integrity. With the exponential growth of social media platforms like Instagram, the issue of spam and fake user accounts has become increasingly prevalent, posing significant challenges to the integrity and user experience of these platforms. In this project, a comprehensive approach to address the problem of spammer detection and fake user identification on Instagram is proposed. Leveraging machine learning techniques, including Decision Tree Classifier, KNN Classifier, Random Forest Classifier, and Logistic Regression Algorithm, the project aims to develop robust models capable of automatically identifying and flagging suspicious accounts. By analyzing various features such as user behaviour patterns, engagement metrics, and content characteristics, these classifiers will be trained to differentiate between genuine and fake accounts effectively. The project's ultimate goal is to contribute to the enhancement of Instagram's spam detection mechanisms, fostering a safer and more authentic social media environment for users. Key Words: Spam detection, Fake user identification, KNN Classifier, Decision Tree Classifier, Random Forest Classifier, Logistic Regression Algorithm.