ABSTRACT The rapid growth of social media platforms such as Instagram has significantly transformed digital communication and online marketing. However, this growth has also led to an alarming increase in fake accounts created for spamming, impersonation, fraud, and misinformation dissemination. These malicious accounts negatively impact user trust, platform credibility, and cybersecurity. This research proposes a machine learning–based approach to detect fake Instagram accounts using profile-based and behavioral features. A dataset of 1,200 Instagram profiles (700 real and 500 fake) was analyzed. Features such as follower-following ratio, posting frequency, engagement rate, profile completeness, and activity patterns were extracted and used to train multiple classification models including Logistic Regression, Support Vector Machine (SVM), and Random Forest. Experimental results demonstrate that the Random Forest classifier achieved the highest accuracy of 93.2%, outperforming other models. The proposed system provides an effective and scalable solution for automated fake account detection.
v et al. (2026) studied this question.