Instagram Fake Account Detection Based on Machine Learning

The rapid growth of social media platforms such as Instagram has significantly transformed digital communication, online interaction, and digital marketing activities. However, this rapid expansion has also led to a substantial increase in the number of fake accounts created for malicious purposes such as spamming, impersonation, online fraud, phishing, and the spread of misinformation. These fake profiles negatively affect user trust, reduce the credibility of social media platforms, and pose serious cybersecurity threats to both individuals and organizations. As manual detection of fake accounts becomes increasingly difficult due to the large volume of users, automated detection methods have become essential. This research proposes a machine learning–based approach to detect fake Instagram accounts using both profile-based and behavioral features. A dataset consisting of 1,200 Instagram profiles, including 700 genuine accounts and 500 fake accounts, was collected and analyzed. Several important features such as follower–following ratio, posting frequency, engagement rate, profile completeness, account activity patterns, and username characteristics were extracted and used as input variables for model training.