ALERT: Active Learning and Explainable AI for Robust Threat Detection in Telegram

The increasing regulatory scrutiny of social media, particularly regarding extremist content and misinformation, underscores the need for advanced threat detection systems. This paper presents ALERT (Active Learning and Explainable AI for Robust Threat Detection in Telegram), a novel framework that enhances threat classification by introducing refined categories and creating tailored datasets 1 . ALERT processes 2,301,110 replies from 17 Telegram channels, focusing on extreme content, with a dataset that predominantly reflects far-right discourse, consistent with activity trends on the platform. By leveraging an iterative active learning approach, it reduces labeling efforts by 86.5%, yielding a labeled dataset of 15,076 replies. ALERT's RoBERTa+ model, pre-trained on domain-specific data, achieved over 90% in precision, recall, accuracy, and F1-score, demonstrating strong generalization for threat detection. The integrated explainable AI (XAI) modules highlight key text features driving model predictions, ensuring transparency while maintaining performance. ALERT offers significant improvements in classification precision and user confidence, providing a critical tool 2 for addressing digital threats while navigating regulatory and privacy challenges.