A Machine Learning Approach for Identifying Fake Accounts on Instagram

Abstract

Instagram is an increasingly popular social media platform in the digital social media ecosystem; however, the issue of fake and robotic accounts gaining momentum is dangerous and accordingly presents misinformation, scams, and identity abuse as significant threats. This study introduces an Instagram Fake Profile Detection system built on machine learning to detect suspicious users accounts with the help of behavioral, numerical, and profile-based features. The given system derives significant predictors, such as the ratio of followers to followed, the articles of the biography, the number of media, the structuring of the usernames, the presence of the profile picture, and privacy settings, and learns the trends typical of fake profiles. Several machine learning models are trained and compared, such as the Random Forest, SVM, ANN, GRU, LSTM, and Hybrid Deep Learning architecture, to increase the prediction accuracy. It adopts a Flask-based web interface enabling real-time classification and visualization of the model outputs of any instagram user input. The findings demonstrate that the reliability of fake account detection is enhanced due to the integration of traditional ML and deep learning networks. In general, this work would be a valuable addition to the body of research because it presents a functional, automatized, and scalable method of enhancing security and trust within the user space of Instagram.

Introduction

The rapid growth of Instagram has led to a significant increase in fake and automated accounts that spread misinformation, promote scams, and impersonate real users, threatening platform authenticity and user safety. Manual detection methods based on profile inspection are inefficient and unreliable at scale, creating the need for automated and accurate solutions. This study proposes an Instagram fake profile detection system using machine learning and deep learning techniques to automatically classify accounts as real or fake.

The system extracts key profile-based features such as follower–following ratio, bio length, number of posts, username structure, presence of profile picture, and account privacy status. Multiple models are trained and compared, including Random Forest, SVM, ANN, and advanced deep learning models such as GRU, LSTM, and a Hybrid architecture, to capture both static and behavioral patterns of user accounts.

A Flask-based web application is developed to provide real-time predictions. Users can input Instagram profile details and instantly receive classification results, along with visual comparisons of predictions across different models. This enhances transparency and usability while demonstrating model performance.

The literature review supports the effectiveness of combining profile metadata with advanced ML/DL models, particularly hybrid architectures, for robust fake account detection. The methodology follows an end-to-end pipeline involving data collection, feature engineering, preprocessing, model training, evaluation, and deployment.

Conclusion

The purpose of this research is to demonstrate how Machine Learning can be applied to determine if a user on Instagram is fake, using different combinations of classifiers. The classifiers include Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), and Hybrid Classifier. These classifiers work together to provide accurate and reliable results based on various characteristics of Users\\\' Profiles. Additionally, the research provides a web application that allows Users to check whether their own profile is real or fake. The web application has an easy-to-use interface with the ability to create a profile, log into the web application, view the results from the Classification and check if their profile is real/fake. In conclusion, this research is an example of how Machine Learning can provide an efficient method for detecting Fake Accounts on Instagram, while simultaneously creating a safer Digital World for all users.

References

[1] Stefanos Chelas, George Routis, Ioanna Roussaki “Detection of Fake Instagram Accounts via Machine Learning Techniques”Submission received: 20 September 2024 / Revised: 6 November 2024 / Accepted: 11 November 2024 / Published: 15 November 2024 https://doi.org/10.3390/computers13110296 [2] Najla Alharbi, Bashayer Alkalifah, Ghaida Alqarawi, Murad A. Rassam “Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection” Submission received: 8 September 2024 / Revised: 28 September 2024 / Accepted: 8 October 2024 / Published: 11 October 2024 https://doi.org/10.3390/fi16100367 [3] Pegah Azami, Pegah Azami, “Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms” Algorithms 2024, 17(10), 425; https://doi.org/10.3390/a17100425 [4] Yunchong Liu, Xiaorui Shen, Yeyubei Zhang, Zhongyan Wang, Yexin Tian, Jianglai Dai & Yuchen Cao “systematic review of machine learning approaches for detecting deceptive activities on social media: methods, challenges, and biases” https://doi.org/10.48550/arXiv.2410.20293

Copyright

Copyright © 2026 Prof. S. N. Pawar, Om Sudhakar Chaudhari, Khushal Sunil Lohar, Kundan Gajanan Patil, Prathamesh Sanjay Khairnar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.