Machine Learning Approaches for Instagram Fake Profile Detection

Bhupendra Kumar Kumbhkar1, Dr. Ajay Kushwaha2

1MTech Scholar, Department of Computer Science & Engineering, RCET Bhilai

2Professor, Department of Computer Science & Engineering, RCET Bhilai

Abstract - Social media networks have grown strongly and greatly changed how we communicate and interact online. As one of the largest social media networks, Instagram has millions of users globally. However, as Instagram continues to grow in popularity, there is an increasing number of people that create fake accounts for malicious purposes such as sending spam, phishing, stealing someone’s identity, spreading misinformation, conducting fraud, and cyberbullying. Fake accounts are harmful to Instagram users and erode user trust, damage the credibility of the platform, and hinder cybersecurity.

It is difficult to identify fake Instagram accounts manually because of the large volume of profiles created every day. A way to automate the detection process on Instagram is by using machine learning (ML) techniques, which can provide an effective, scalable solution to detect suspicious accounts using profile characteristics, user behaviour, and engagement patterns. In this paper, we propose a ML-based system to detect fake Instagram accounts based on supervised learning algorithms. We employ data pre-processing, feature extraction, model training, evaluation, and deployment through a web interface. The proposed system provides an effective solution for identifying fake Instagram profiles and can assist social media platforms, cybersecurity organizations, and users in reducing fraudulent activities. Future improvements may involve deep learning models, behavioral analysis, and real-time detection systems.

Keywords: Instagram, Fake Account Detection, Machine Learning, Random Forest, Cybersecurity, Social Media Analytics, Classification.