Moderating usernames seems simple—block bad words. In practice, users constantly find creative ways to bypass filters, making sophisticated detection essential.
Why Simple Filters Fail
Basic keyword matching catches obvious profanity but misses: leetspeak (a55, sh1t), character substitutions (@ for a, 0 for o), spacing tricks (bad word), intentional misspellings (phuck), and hidden words (scunthorpe problem—blocking "ass" rejects "class"). Simple filters also over-block innocent words.
Leetspeak and Evasion Tactics
Leetspeak replaces letters with numbers and symbols: A→4, E→3, I→1, O→0, S→5 or $, T→7. Users combine these with character insertions (a.s.s), Unicode look-alikes (Cyrillic а vs Latin a), and zero-width characters. Detection must normalize and pattern-match across variations.
Context and False Positives
Words aren't always offensive in context: "Scunthorpe" is a town, "assassin" contains "ass" twice, "shitake" is a mushroom. Username detection can't rely on context like text analysis can. Maintaining quality wordlists that minimize false positives while catching evasion is an ongoing challenge.
Multi-Language Considerations
Profanity detection must cover multiple languages. A username offensive in Spanish should be caught even on an English platform. Some character combinations are innocent in one language but offensive in another. Global platforms need comprehensive multilingual detection.