Toward robust malicious group identification in multilingual social networks: a graph representation learning approach

Purpose Multilingual social networks present significant challenges in malicious group detection due to linguistic diversity and heterogeneous data streams amplified by artificial intelligence (AI) and Internet of Things (IoT) technologies. This study aims to propose a novel framework to enhance detection robustness, accuracy and sensitivity in these environments. Design/methodology/approach Leveraging graph representation learning (GRL), the solution integrates multilingual natural language processing, machine learning and structural community discovery. The framework incorporates multimodal data (including user positional information) to optimize real-time monitoring and early warning systems. Findings Quantitative evaluation demonstrates state-of-the-art performance: 95% classification accuracy and 94.88% macro-F1 score for group influence recognition, representing statistically significant improvements of 2.5% (accuracy) and 2.44% (F1) over baselines (p < 0.01). Under high-confidence constraints with positional data, performance increases to 99.17% accuracy and 98.33% macro-F1. Originality/value This work addresses critical gaps in cross-lingual threat detection by unifying GRL with multimodal AI/IoT data processing. The proposed framework advances real-time malicious group identification in Web information systems, offering scalable solutions for security-sensitive platforms.