Fraud Detection in Social Media: Integrating Machine Learning for User and Content Verification

Abstract

Social media platforms have become major vectors for financial and cryptocurrency fraud, resulting in substantial economic losses and eroding user trust. This research presents a dual model system integrating machine learning-based user verification with deep learning-based content analysis to detect fraudulent activity more effectively than traditional single dimensional approaches. The core innovation lies in fusing these two modalities using a logical OR strategy. This design facilitates the detection of hybrid fraud schemes such as compromised legitimate accounts posting deceptive content or fake accounts spreading benign-looking messages that typically evade isolated detection systems. For user verification, ensemble methods were evaluated on a large, balanced dataset of social media profiles. Among the individual classifiers evaluated, the random forest classifier achieved the strongest performance and was selected for the final architecture due to its optimal balance of accuracy, interpretability, and computational efficiency. For content analysis, a convolutional neural network (CNN) trained on a substantial corpus of crypto-related posts demonstrated high accuracy, outperforming traditional keyword-based and recurrent neural network baselines. Ultimately, combining these models yields a system that flags significantly more fraudulent posts than either component alone. The user verification component retains interpretability through feature importance measures, while the CNN-based content analysis component operates as a less transparent classifier; the combined system can be adapted across diverse social media platforms.