The 10 fields and concepts you'll meet in the response — defined in plain English, each with a real example value.
The automated identification of offensive or inappropriate language in text.
Profanity filters compare text against dictionaries of prohibited words, using exact and fuzzy matching to catch variations. Advanced filters handle leetspeak, multi-language content, and context. No filter is 100% accurate—combine with human review for sensitive applications.
ExampleInput: "This is sh1t" → Output: {isProfane: true, wordsFound: ["sh1t"]}
The use of numbers and symbols to replace letters, often to bypass profanity filters.
Common substitutions: 1→i/l, 3→e, 4→a, 5→s, 0→o, $→s, @→a. Example: "a$$hole" instead of "asshole". Advanced profanity filters normalize leetspeak before checking against dictionaries. Complete detection is impossible as users invent new variations.
Examplesh1t (shit), a$$ (ass), fvck (fuck)
The process of automatically reviewing and blocking inappropriate content based on predefined rules.
Filters check text for profanity, spam, hate speech, or other violations. They can auto-censor (replace with asterisks), auto-reject (block submission), or flag for review. Filters use dictionaries, pattern matching, machine learning, or combinations. Balance strictness with false positive rate.
ExampleFilter detects profanity, replaces with: "This is ****"
Identifying unwanted or malicious content, typically promotional messages or bot-generated text.
Spam detection analyzes content patterns (excessive links, keyword stuffing, duplicate text), behavioral signals (posting frequency, new accounts), and link reputation. Machine learning models trained on known spam improve accuracy over time. Combine with rate limiting and CAPTCHA.
ExampleSpam score: 85/100 → Flag for review or auto-remove
Restricting the number of actions a user can perform within a time period to prevent spam and abuse.
Common limits: 5 posts/minute, 100 posts/day, 10 accounts/IP/day. Rate limits prevent spam bots, brute-force attacks, and API abuse. Implement exponential backoff for repeated violations. Legitimate power users may need higher limits—consider tiered systems.
ExampleLimit: 5 comments/minute. User posts 6th → "Slow down, try again in 30 seconds"
Completely Automated Public Turing test to tell Computers and Humans Apart—a challenge to verify users are human.
CAPTCHAs prevent automated spam and abuse. Types include distorted text, image selection (reCAPTCHA), and behavioral analysis (hCaptcha). Use when: creating accounts, submitting forms repeatedly, or exhibiting bot-like behavior. Balance security with user experience—excessive CAPTCHAs annoy legitimate users.
ExampleUser creates account → Show CAPTCHA → Verify before account creation
A moderation technique where a user's content is hidden from others but appears normal to the user.
Shadowbanned users don't know they're banned—their posts are invisible to everyone else. This prevents spammers from immediately creating new accounts. However, it's controversial for transparency reasons. Use for clear spam/bots, not legitimate users.
ExampleSpammer posts content, sees it published, but nobody else can see it
When a filter incorrectly identifies legitimate content as a violation.
The Scunthorpe problem: "Scunthorpe" contains profanity substring but is a legitimate town name. False positives frustrate users and may censor important content. Reduce with context-aware filters, allowlists for known exceptions, and human review for flagged content.
Example"Scunthorpe United" flagged as profanity (false positive)
Using natural language processing to determine the emotional tone of text (positive, negative, neutral).
Sentiment analysis helps identify toxic content beyond profanity. Aggressive or hostile language may not contain profanity but still violates community standards. Machine learning models score text from very negative (-1) to very positive (+1). Combine with profanity filters for comprehensive moderation.
Example"I hate you, you're worthless" → Sentiment: -0.9 (very negative, toxic)
Identifying harmful content including profanity, hate speech, threats, and harassment.
Toxicity detection is broader than profanity filtering. It identifies: insults, threats, hate speech, sexually explicit content, and identity attacks. Google's Perspective API provides toxicity scores. Combine automated detection with human review for accuracy. Define toxicity thresholds based on community standards.
ExampleText scored: Profanity: 0.9, Threat: 0.7, Insult: 0.8 → High toxicity
