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@article{195807,
        author = {D.N.B.T.Sundari and T. Aditi and S. Reshma Reddy and K. Manisha and M. Siddhu Yadhav},
        title = {SPAMMER DETECTION AND FAKE USER IDENTIFICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1057-1067},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195807},
        abstract = {The rapid growth of Online Social Networks (OSNs) such as Twitter and Facebook has significantly transformed the way users communicate and share information. However, this growth has also led to an increase in malicious activities, particularly spam dissemination and the creation of fake user accounts. These threats not only degrade user experience but also compromise platform credibility, security, and trust. Detecting such malicious entities has become a critical challenge due to the dynamic and evolving nature of spam techniques. 
This paper proposes an efficient and scalable system for spammer detection and fake user identification using machine learning and behavioral analysis techniques. The system categorizes spam detection into multiple dimensions, including fake content detection, URL-based spam identification, spam in trending topics, and fake user profiling. It leverages Artificial Neural Networks (ANN) along with other machine learning approaches to classify user behavior and identify suspicious activities with high accuracy.
The proposed framework integrates a secure authentication mechanism, fake login detection using IP tracking, and a real-time monitoring system to enhance platform security. A structured data preprocessing and feature extraction process is implemented to improve model performance by analyzing user interactions, content patterns, and activity behavior. The system is designed to be lightweight, scalable, and capable of operating in real-time environments.
Experimental evaluation demonstrates that the proposed approach achieves high detection accuracy while minimizing false positives. By combining behavioral analytics, machine learning models, and system-level security mechanisms, the proposed solution effectively enhances trust, reliability, and safety in online social networking environments.},
        keywords = {Spammer Detection, Fake User Identification, Machine Learning, Artificial Neural Networks, Social Networks, Cybersecurity},
        month = {April},
        }