Ensuring Social Media Authenticity: Leveraging Machine Learning
Abstract
Identity fraud continues to be a major concern in contemporary online social networks. Research initiatives emphasize developing technologies to detect identity fraud; however, their effectiveness often depends on empirical validation. This study explores identity fraud detection through clustering and classification methods, aiming to overcome traditional methodological shortcomings and propose enhancements for practical application. Data is collected from social media accounts and processed through steps such as Natural Language Processing (NLP), vectorization, dimensionality reduction, and data normalization. Behavioural analysis and profile attributes are leveraged for feature extraction. Profiles are classified as genuine or fraudulent using clustering approaches, followed by deep learning classification on similar datasets.
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