Building AI-Powered Spam Detection for Telegram with 99.7% Accuracy

This article describes how the authors built a proprietary AI-based spam detection system for Telegram communities, achieving 99.7% accuracy and near-zero false positives.

Details

The authors faced the challenge of spam in Telegram communities, with existing solutions relying on keyword filtering that was easily bypassed by smart spammers. They built a proprietary AI-powered spam detection system that analyzes the full context of messages, including chat topic, user history, and profile metadata, to understand the true intent behind each message. The system has multiple layers, including pre-message analysis, a message context engine, edit detection, a global ban network, and a user trust system. By building a custom AI model specifically for the Telegram domain, the authors achieved 99.7% spam detection accuracy and a near-zero false positive rate, processing millions of messages with sub-second decision times. Key technical decisions included using a proprietary AI model over third-party APIs, leveraging a global network over isolated instances, and taking a trust-based approach over rule-based. The authors are now expanding the system to handle voice messages, image content, and behavioral prediction.