Profanity detection identifies offensive language in text. While seemingly simple, it requires handling multiple languages, context awareness, and creative bypasses like leetspeak.
Dictionary-Based Detection
Most filters use dictionaries of prohibited words. Text is compared against this list for matches. Advantages: fast, simple, predictable. Disadvantages: easily bypassed (add spaces, use leetspeak), misses context (profanity in quotes vs direct use), requires maintenance as language evolves. Dictionary-based detection catches 70-80% of violations in isolation.
Pattern Matching and Leetspeak
Advanced filters use fuzzy matching to catch variations: "a$$hole", "sh1t", "fvck". They replace common substitutions (0→o, 1→i, $→s) then check against dictionaries. This catches creative spelling but may produce false positives (Scunthorpe problem: legitimate words containing profanity substrings). Balance strictness with accuracy.
Multi-Language Profanity
Different languages have different offensive terms. A global platform needs dictionaries for each supported language. Some words are profane in one language but not another. Automatic language detection (via API or user profile) ensures the correct dictionary is used. Without multi-language support, users can bypass filters by switching languages.