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@article{175702,
        author = {K HARI KRISHNA and M CHATRAPATHI and K GOWTHEESWAR RAO and Mr.O G SURESH KUMAR and Mr.PANDRETI PRAVEEN and Dr.R KARUNIA KRISHNAPRIYA and Mr.V SHAIK MOHAMMAD SHAHIL and Mr. N.VIJAYA KUMAR},
        title = {Leveraging Machine Learning Techniques for Detecting Fake Profiles in Online Social Platforms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4171-4176},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175702},
        abstract = {The sudden rise of online social sites has enabled global communication and information sharing but has also resulted in an upsurge of fake profiles, which carry serious threats like misinformation, identity theft, and privacy violation. This paper investigates the use of machine learning algorithms to efficiently identify and categorize fake profiles on social networks. We introduce an extensive examination of several features widely linked to deceptive accounts, such as behavioural traits, network attributes, and content-based features. A variety of supervised and unsupervised machine learning techniques, including decision trees, support vector machines, random forests, and clustering algorithms, are tested in terms of their ability to identify spurious profiles. Experimental results on live social media datasets reveal the very high accuracy and reliability of such models in separating real users from spammers. The results shed light on the effectiveness of machine learning as a strong tool for raising the security and integrity level of social media ecosystems.},
        keywords = {Fake Profile Detection, Machine Learning, Online Social Networks, Social Media Security, User Profiling, Anomaly Detection, Bot Detection, Identity Fraud, Classification Algorithms, Feature Engineering, Data Mining, Spam Detection, Behavioural Analysis, Deep Learning, Cybersecurity},
        month = {April},
        }