Published Paper Details:

The rapid growth of social media platforms has significantly increased the spread of online scams, phishing attacks, fake advertisements, fraudulent investment schemes, and impersonation attacks. Traditional scam detection systems primarily focus on textual analysis and fail to identify complex multimodal scams that combine text, images, videos, and malicious URLs. This research proposes a Multi-Modal Scam Detection System using Explainable Artificial Intelligence (XAI) to improve the accuracy, transparency, and reliability of scam detection in social media environments. The proposed framework integrates Natural Language Processing (NLP), image feature extraction, metadata analysis, and machine learning techniques for identifying fraudulent content. The system utilizes transformer-based text classification, Convolutional Neural Networks (CNN) for image analysis, and ensemble learning methods for multimodal fusion. Explainable AI techniques such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are incorporated to provide interpretable predictions and increase user trust. Experimental results demonstrate that the proposed model achieves higher accuracy, precision, recall, and F1-score compared with traditional machine learning methods. The research contributes toward secure digital communication and reliable social media monitoring systems.