Semantic Anti-Spam Outperforms Keyword Filters for Telegram Group Moderation

Telegram community admins often struggle with promotional links, phishing, and repetitive junk that slip past traditional keyword blocklists. A new guide compares these static filters, which are fast but easily evaded and prone to false positives, with semantic anti-spam that uses large language models to judge a message's meaning. The semantic approach assigns a spam probability to each message and acts when it crosses a configurable threshold, catching spam without banned words across multiple languages. The article also covers the tradeoffs, including added latency and token costs, and walks through enabling the semantic filter in the AIGB bot alongside a lightweight keyword list.