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@article{178231,
        author = {Keerthanaa B and Nanabala Shreyashree and Divya Jyothi S and Laxmi R Walimarad},
        title = {FAKE ACCOUNT DETECTION IN SOCIAL MEDIA USING MACHINE LEARNING AND DJANGO FRAMEWORK},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4059-4063},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178231},
        abstract = {The rise of fake accounts on social media platforms poses serious threats to online communication, marketing integrity, and user privacy. This project proposes a machine learning-based solution integrated within a Django web application to detect such accounts. By analyzing user profile features, behavioral patterns, and interaction data, the system effectively distinguishes real users from fraudulent ones. Models like Support Vector Machines (SVM) and Neural Networks are trained on curated datasets and evaluated using precision, recall, and F1-score. The backend includes data preprocessing, feature extraction, and transformation for model training. These models are deployed through a RESTful API using Django REST Framework, enabling real-time predictions. This hybrid behavioral and content-based approach ensures scalable and accurate fake account detection for safer social media environments.},
        keywords = {Fake Account Detection, Machine Learning, Support Vector Machine (SVM), Neural Networks, Django REST Framework, Social Media Security, Behavioral Analysis, Real-time Prediction, Natural Language Processing (NLP), Data Preprocessing},
        month = {May},
        }