Year: 2025
Type: article
Abstract: With the exponential rise of fake accounts on social media platforms, malicious entities are now exploiting sophisticated techniques to mimic real user behavior and deceive systems.This paper proposes a robust Multi-Modal Fake Profile Detection System that combines three powerful analytical dimensions: natural language processing (NLP), image-based deep learning, and behavioral pattern analysis.Each module processes a different data modality -such as textual content, profile images, and activity logs -and outputs an independent score.These are then fused via a weighted decision model to provide the final classification: REAL or FAKE.The model achieved 95% accuracy through ensemble fusion, outperforming individual modality performance.The system also integrates explainability using SHAP values and a Flask-based web interface for real-time prediction and visualization.The framework offers a scalable and interpretable solution to combat fake profiles across platforms.
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414207102
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