Content Moderation Best Practices (2025)

A comprehensive guide to implementing content moderation in modern applications.

The Modern Moderation Stack

Content moderation in 2025 requires:

1. Multi-modal coverage - Text, images, and video
2. Real-time decisions - Sub-second response times
3. Configurable policies - Different rules for different contexts
4. Audit trails - Every decision logged and reviewable
5. Human escalation - AI + human review for edge cases

Core Principles

1. Defense in Depth

Never rely on a single layer of protection:

User Input → Client Validation → API Moderation → Database Rules → Human Review

Each layer catches what the previous missed.

2. Fail Gracefully

Decide your failure mode upfront:

try {
  const result = await vettly.check({ content, contentType: 'text' })
  if (result.action === 'block') {
    return reject(content)
  }
} catch (error) {
  // Choose one:
  // Option A: Fail closed (safer, may block legitimate content)
  return reject(content)

  // Option B: Fail open (riskier, but better UX)
  return allow(content)
}

For high-risk contexts (payments, legal), fail closed. For social features, consider failing open with async review.

3. Context Matters

A message saying "kill it!" means different things in:

• A gaming chat (probably fine)
• A support ticket (needs review)
• A threat report (escalate immediately)

Configure different policies for different contexts:

# gaming-chat.yaml
categories:
  violence:
    threshold: 0.9  # Very permissive
    action: flag

# support-tickets.yaml
categories:
  violence:
    threshold: 0.5  # More sensitive
    action: block

Text Moderation

Best Practices

1. Moderate before storage - Check content before saving to database
2. Include context - Send surrounding messages for better accuracy
3. Handle edits - Re-moderate when users edit content
4. Rate limit - Prevent abuse with request limits

Example Flow

// 1. Check content
const result = await vettly.check({
  content: userMessage,
  contentType: 'text',
  policyId: 'community-chat'
})

// 2. Handle decision
switch (result.action) {
  case 'block':
    // Don't save, notify user
    return { error: 'Message not allowed' }

  case 'flag':
    // Save but queue for review
    await db.messages.create({
      content: userMessage,
      flagged: true,
      moderationId: result.decisionId
    })
    break

  case 'allow':
    // Save normally
    await db.messages.create({ content: userMessage })
    break
}

Image Moderation

Key Considerations

1. Check before upload - Moderate images before saving to storage
2. Handle all formats - JPEG, PNG, GIF, WebP
3. Size limits - Set reasonable file size limits
4. Async for large files - Use webhooks for videos and large images

Example Flow

// 1. Receive upload
const file = await request.formData()
const image = file.get('image')

// 2. Convert to base64
const buffer = await image.arrayBuffer()
const base64 = Buffer.from(buffer).toString('base64')

// 3. Moderate
const result = await vettly.check({
  content: base64,
  contentType: 'image'
})

// 4. Handle decision
if (result.action === 'block') {
  return { error: 'Image not allowed' }
}

// 5. Upload to storage only if allowed
await uploadToS3(image)

Video Moderation

Challenges

• Larger files = longer processing
• Frame-by-frame analysis needed
• Audio track may contain violations

Recommended Approach

1. Use webhooks - Don't wait synchronously
2. Hold uploads - Store in temp location until moderated
3. Show status - Let users know content is being reviewed

// 1. Upload to temp storage
const tempUrl = await uploadToTemp(video)

// 2. Start async moderation
const result = await vettly.check({
  content: tempUrl,
  contentType: 'video',
  webhook: 'https://yourapp.com/webhooks/moderation'
})

// 3. Return pending status
return {
  status: 'processing',
  jobId: result.decisionId
}

// 4. Webhook handler moves to permanent storage if allowed

Policy Design

Start Strict, Then Loosen

It's easier to relax rules than tighten them:

# Start with strict defaults
default_action: flag

categories:
  hate:
    threshold: 0.5
    action: block
  harassment:
    threshold: 0.6
    action: block
  violence:
    threshold: 0.7
    action: flag

Monitor false positives, then adjust thresholds upward.

Separate Policies by Context

Don't use one policy for everything:

Context	Policy	Approach
User profiles	strict	Block most violations
Private messages	balanced	Flag for review
Public posts	strict	Protect community
Gaming chat	permissive	Allow banter

Version Control Policies

Store policies in git:

policies/
  production/
    default.yaml
    strict.yaml
    permissive.yaml
  staging/
    experimental.yaml

Changes are reviewable, reversible, and auditable.

Human Review

When to Escalate

AI isn't perfect. Escalate when:

• Confidence scores are borderline (0.4-0.6)
• Content is flagged but not blocked
• User appeals a decision
• New violation patterns emerge

Review Queue Design

// Queue flagged content for review
if (result.action === 'flag') {
  await reviewQueue.add({
    contentId: content.id,
    decisionId: result.decisionId,
    categories: result.categories,
    priority: calculatePriority(result)
  })
}

Reviewer Guidelines

• Provide clear accept/reject criteria
• Show context (previous messages, user history)
• Track reviewer accuracy
• Rotate difficult categories

Logging and Auditing

What to Log

Every moderation decision should include:

{
  "decisionId": "dec_abc123",
  "timestamp": "2025-01-15T10:30:00Z",
  "contentType": "text",
  "action": "block",
  "categories": [
    { "category": "harassment", "score": 0.87 }
  ],
  "policyId": "strict",
  "userId": "user_123",
  "contentHash": "sha256:..."
}

Retention

• Keep decision logs for compliance (typically 1-7 years)
• Store content hashes, not raw content when possible
• Enable audit exports for legal requests

Performance

Optimize for Latency

1. Cache policies - Don't fetch on every request
2. Batch when possible - Multiple items in one call
3. Use regional endpoints - Reduce network latency
4. Async for non-blocking - Use webhooks for background checks

Target Metrics

Metric	Target
P50 latency (text)	< 100ms
P99 latency (text)	< 500ms
P50 latency (image)	< 1s
Availability	99.9%

Handling Edge Cases

User Appeals

Always provide an appeal path:

// Store decision ID with content
await db.posts.create({
  content,
  moderationDecisionId: result.decisionId
})

// Appeal endpoint
app.post('/appeal', async (req, res) => {
  const { postId, reason } = req.body
  const post = await db.posts.findById(postId)

  await reviewQueue.add({
    type: 'appeal',
    decisionId: post.moderationDecisionId,
    reason,
    priority: 'high'
  })
})

False Positives

Track and learn from mistakes:

1. Log all overturned decisions
2. Identify patterns in false positives
3. Adjust thresholds based on data
4. Consider category-specific tuning

Adversarial Content

Users will try to bypass moderation:

• Unicode tricks (homoglyphs)
• Leetspeak (h4t3)
• Spacing tricks (h a t e)
• Image text (text embedded in images)

Modern APIs handle most of these, but stay vigilant with new patterns.

Compliance

GDPR / Privacy

• Log decisions, not content when possible
• Enable data deletion requests
• Provide decision explanations to users

Platform Requirements

If you're building on platforms:

• App Store - Requires user-generated content moderation
• Play Store - Similar requirements
• Discord - Must moderate for ToS compliance

Legal Holds

Enable freezing of moderation data for legal requests:

// Mark content for legal hold
await db.moderationLogs.update({
  where: { decisionId },
  data: { legalHold: true, holdExpiry: null }
})

Getting Started with Vettly

Implement these best practices with Vettly:

npm install @nextauralabs/vettly-sdk

import { ModerationClient } from '@nextauralabs/vettly-sdk'

const vettly = new ModerationClient({
  apiKey: process.env.VETTLY_API_KEY
})

// That's it. Start moderating.
const result = await vettly.check({
  content: userInput,
  contentType: 'text'
})

Summary

1. Layer your defenses - Client, API, database, human
2. Configure per context - Different rules for different features
3. Log everything - Decisions, appeals, overrides
4. Plan for humans - AI + human review for edge cases
5. Start strict - Easier to loosen than tighten

Content moderation is not a one-time setup. It's an ongoing practice that evolves with your community and the threat landscape.