Identification of Spammers and Fake Users in Social Networks Using Machine Learning

The integrity of online social networks is seriously threatened by spammers and phony users, especially on sites like Instagram where user-generated content and interactions are vital in determining engagement. Through the analysis of behavioural, content-based, and network-driven features, this project suggests a machine learning-based method for the automated detection of spammers and phony accounts. A supervised learning model is created and trained to accurately differentiate between spam accounts and real users. Through an easy-to-use interface, users and administrators can upload datasets or keep an eye on user activity thanks to the trained model’s integration into a web application developed with the Django framework. The system facilitates early detection of malicious accounts and helps to maintain a trustworthy online environment by offering instant classification results. This work shows how web technologies and machine learning can be combined to create scalable, useful, and easily accessible social network security tools. Automated detection systems are essential for protecting user experience as social media platforms are increasingly being abused for malicious activities, spam, and scams. In user behaviour analysis and anomaly detection, machine learning algorithms—in particular, ensemble approaches and classification models—have demonstrated encouraging results. But manually keeping an eye on millions of accounts is unfeasible and prone to errors. The suggested system reduces human error, speeds up the detection of fraudulent accounts, and builds trust in online communities by automating the detection process.