Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{179386,
        author = {Mythri Chanda and Manasa Suresh and Lakshmi Preethi and Hemanth N and keerthana P},
        title = {Detection of Fake Social Media Accounts Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6877-6881},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179386},
        abstract = {The proliferation of fake social media accounts poses a significant challenge to online security, trust, and authenticity. These fraudulent accounts are often used for malicious activities such as misinformation propagation, phishing, and spamming, necessitating robust detection mechanisms. This paper presents a machine learning-based approach for the detection of fake social media accounts using a dataset containing real and synthetic user profiles. The dataset is preprocessed using advanced techniques, including handling missing values, feature scaling, and text vectorization to enhance data quality.
This research contributes to the ongoing efforts to enhance digital platform security by providing an efficient and scalable detection framework. The findings suggest that a hybrid approach combining rule-based and machine learning techniques can further improve detection accuracy. Future research will focus on integrating real-time detection mechanisms, leveraging graph-based anomaly detection, and expanding datasets to improve generalizability across multiple platforms and languages.},
        keywords = {Fake accounts, Machine learning, social media security},
        month = {May},
        }