Enhancing Social Media User's Trust: A Comprehensive Framework for Detecting Malicious Profiles Using Multi-Dimensional Analytics
DOI:
https://doi.org/10.46647/rdems0205003Keywords:
Graph Neural Networks (GNN), Social Network Analysis (SNA), Fake Profile Detection, Botnet Detection, Online Social Networks, Deep Learning, Graph Representation Learning, Node Classification, Edge Prediction, Community Detection, Cybersecurity, Misinformation Detection, Behavioral Pattern Analysis, Machine Learning for Security, Indian Social Media Ecosystem, Network Anomaly Detection, Graph Embedding, Fraud Detection, Automated Bot Identification, Digital Trust and Safety.Abstract
The rapid proliferation of social media platforms in India has led to a significant rise in fake profiles and coordinated botnets, posing serious threats to digital trust, public discourse, and cybersecurity. Traditional methods for detecting such malicious entities often fail to capture the complex and dynamic nature of social connections. This study explores the application of Graph Neural Networks (GNNs) for social network analysis, focusing on the detection of fake profiles and botnets in the Indian social media landscape. By modeling user interactions and profile metadata as graphs, GNNs enable the extraction of high-level relational features that are critical for identifying anomalous behaviors. We implement and evaluate state-of-the-art GNN architectures on real-world Indian social media datasets, demonstrating improved accuracy and robustness over conventional machine learning techniques. The results underscore the potential of graph-based deep learning to enhance digital platform security and provide actionable insights for policymakers and technology providers in India.