Implementing 3-Tier Moderation for Telegram Bots
Binary spam detection (spam or not spam) breaks down in active communities. A forwarded giveaway could be spam or a legitimate user sharing excitement. A message saying "write me" could be a scam C...
Source: DEV Community
Binary spam detection (spam or not spam) breaks down in active communities. A forwarded giveaway could be spam or a legitimate user sharing excitement. A message saying "write me" could be a scam CTA or an angry user. We rebuilt our Telegram moderation pipeline into three action tiers using Claude Haiku for intent classification: tier 1 (ban) for clear spam with profit intent, tier 2 (mute + admin buttons) for ambiguous cases, and tier 3 (3-strike warnings) for links and forwards. The system also auto-detects chat language from linked channel posts using character-set heuristics (їєґ → Ukrainian, ыэъ → Russian). How do you handle the gray area between spam and legitimate messages in your moderation systems? Telegram Stories / Short caption (RU)
