Design a Content Moderation System

Build an AI-powered content moderation system that combines automated detection with human oversight to enforce community guidelines at social media scale.

System Requirements

Functional Requirements

Multi-modal content analysis (text, images, video, audio)
Real-time and batch content processing
Policy rule engine with customizable guidelines
Human review workflow for edge cases
Appeal and content restoration process
User reporting and community flagging
Content categorization and risk scoring
Automated actions (hide, delete, restrict, warn)

Non-Functional Requirements

10M+ posts processed per day
Sub-second response time for real-time moderation
99.5% accuracy for policy violations
<0.1% false positive rate on legitimate content
Support 50+ languages and cultural contexts
99.99% system availability
Scale during viral content spikes
GDPR/privacy compliance for user data

Content Moderation Strategies

Rule-Based Filtering

70-80% accuracy

Predefined rules and keyword matching for policy violations

Pros

• Fast processing
• Explainable decisions
• Easy policy updates

Cons

• Limited context understanding
• Easy to circumvent
• High maintenance

Best For

Spam, explicit keywords, known violation patterns

Accuracy

70-80%

ML Classification Models

85-95% accuracy

Supervised models trained on labeled violation data

Pros

• Context-aware
• Learns from data
• Scales with training

Cons

• Requires labeled data
• Bias issues
• Black box decisions

Best For

Hate speech, harassment, misinformation detection

Accuracy

85-95%

Computer Vision Analysis

80-90% accuracy

Deep learning models for image and video content

Pros

• Detects visual violations
• Handles multimedia
• Evolving capabilities

Cons

• Computationally expensive
• Context limitations
• Adversarial attacks

Best For

Nudity, violence, inappropriate imagery

Accuracy

80-90%

Human-in-the-Loop

95-99% accuracy

Human moderators review flagged or uncertain content

Pros

• Highest accuracy
• Context understanding
• Cultural sensitivity

Cons

• Expensive
• Slow
• Psychological toll on moderators

Best For

Edge cases, appeals, high-stakes content

Accuracy

95-99%

Capacity Estimation

Content Moderation Scale

Moderation Split

85% of contentAutomated

15% of contentHuman Review

Content Types

60% splitText

40% splitMultimedia

Action Distribution

95% of contentApproved

5% of contentFlagged/Removed

Content Processing

Daily Content Volume

Social media platform scale

10M posts

Peak Processing Rate

Viral content spike handling

500 posts/sec

Response Time

Real-time moderation latency

< 1 second

False Positive Rate

Legitimate content wrongly flagged

< 0.1%

Infrastructure Scale

ML Processing

50+ GPU instances, 10+ model types

Human Reviewers

1K+ moderators, 24/7 coverage

Data Storage

1PB+ content archive, 6 month retention

Content Policy Framework

Safety Violations

High Risk

Examples

• Violence and incitement
• Self-harm content
• Dangerous organizations

Action

Immediate removal

Review Required

Automated only

Hate Speech

High RiskHuman Review

Examples

• Targeted harassment
• Discriminatory language
• Identity-based attacks

Action

Remove + Warning

Review Required

Yes

Misinformation

Medium RiskHuman Review

Examples

• False health claims
• Election misinformation
• Conspiracy theories

Action

Label + Reduce reach

Review Required

Yes

Spam & Manipulation

Medium Risk

Examples

• Bot networks
• Coordinated inauthentic behavior
• Fake engagement

Action

Account restrictions

Review Required

Automated only

Adult Content

Low Risk

Examples

• Nudity
• Sexual content
• Adult services

Action

Age-gate + Warning

Review Required

Automated only

System Architecture

Content Flow

User Post → Content Ingestion → Pre-processing → ML Analysis

↓

Policy Engine → Risk Scoring → Action Decision

↓

Auto-Action OR Human Review Queue → Final Decision → User Notification

Content Ingestion Pipeline

Apache Kafka, message queues, stream processing

Purpose:

Real-time processing of user-generated content

Scale:

100K+ posts per minute

ML Model Serving

TensorFlow Serving, GPU clusters, model ensembles

Purpose:

Real-time inference for policy violation detection

Scale:

1M+ predictions per hour

Human Review Platform

Web dashboard, task assignment, quality assurance

Purpose:

Workflow management for human moderators

Scale:

10K+ cases reviewed daily

Policy Engine

Rules engine, decision trees, action workflows

Purpose:

Business rules and action enforcement

Scale:

24/7 policy evaluation

Technical Challenges & Solutions

Cultural Context & Language

Problem: Same content may be acceptable in one culture but not another

Solution: Localized models and region-specific policy rules

Implementation: Multi-cultural training data, regional model variants

Adversarial Content

Problem: Users deliberately try to evade detection systems

Solution: Adversarial training and continuous model updates

Implementation: Red team exercises, dynamic model retraining

Scale vs Accuracy Trade-off

Problem: Need to process millions of posts with high accuracy

Solution: Tiered moderation with different precision levels

Implementation: Fast pre-filtering + detailed analysis for flagged content

Moderator Wellbeing

Problem: Human reviewers exposed to traumatic content

Solution: Wellness programs and content pre-filtering

Implementation: Rotation schedules, mental health support, automated screening

Data Architecture

Content Schema

content:
- content_id (uuid, PK)
- user_id (uuid, indexed)
- content_type (text/image/video)
- content_hash (varchar)
- raw_content (text/blob)
- created_at (timestamp)
- moderation_status (enum)
- risk_score (float 0-1)
- policy_violations (jsonb)
- human_reviewed (boolean)

Moderation Actions

moderation_actions:
- action_id (uuid, PK)
- content_id (uuid, FK)
- action_type (hide/delete/warn/flag)
- reason_code (varchar)
- automated (boolean)
- moderator_id (uuid, nullable)
- created_at (timestamp)
- reversed (boolean)
- appeal_status (enum)

Practice Questions

How would you handle cultural differences in content moderation across global markets?

Design a system to detect coordinated inauthentic behavior across multiple accounts.

How would you balance automated efficiency with human oversight for content appeals?

Design a multi-modal AI system that can understand context across text, images, and video.

How would you prevent adversarial attacks where users try to evade moderation systems?