What This Template Is For
Content moderation is the system that decides what stays up, what comes down, and how fast. Every platform that accepts user-generated content needs a moderation policy and the technical systems to enforce it. Without a written specification, moderation decisions become inconsistent, response times balloon, and the platform risks both user harm and regulatory exposure.
This template covers the full stack of content moderation: policy definitions, automated detection, human review workflows, appeals processes, and enforcement actions. It is designed for product managers building or scaling moderation systems on social, community, marketplace, or media platforms. If your platform uses AI for automated detection, the AI Ethics Review Template pairs well with this document. For a deeper look at building responsible AI features, see the Responsible AI Framework.
How to Use This Template
- Copy the blank template into your documentation tool.
- Start with the Content Policy Tiers section. Define what content is prohibited, restricted, and allowed before building any systems.
- Work through the Automated Detection section with your ML and engineering leads. Be realistic about what automation can and cannot catch.
- Define the Human Review Workflow next. Automated systems catch volume, but human reviewers handle nuance.
- Build the Appeals Process before launch. Users who feel unfairly moderated and have no recourse become vocal critics.
- Review the full spec with Legal, Trust & Safety, and your customer support team.
The Template
Content Policy Tiers
Define clear categories with specific examples. Ambiguous policies lead to inconsistent enforcement.
Tier 1: Prohibited Content (Immediate Removal)
- ☐ [Category]: [Description and examples]
- ☐ [Category]: [Description and examples]
- ☐ [Category]: [Description and examples]
- ☐ [Category]: [Description and examples]
- ☐ Legal/regulatory requirements that mandate removal: [List jurisdictions and requirements]
Tier 2: Restricted Content (Review Required)
- ☐ [Category]: [Description, conditions under which it may be allowed]
- ☐ [Category]: [Description, conditions under which it may be allowed]
- ☐ [Category]: [Description, conditions under which it may be allowed]
Tier 3: Allowed with Conditions
- ☐ [Category]: [Description, required labels or age gates]
- ☐ [Category]: [Description, required labels or age gates]
Automated Detection System
- ☐ Detection Methods:
- ☐ Text classification: [Model type, languages supported, accuracy targets]
- ☐ Image/video analysis: [Model type, detection categories, false positive tolerance]
- ☐ Audio analysis: [If applicable, describe approach]
- ☐ Metadata/behavioral signals: [Spam patterns, account age, posting velocity]
- ☐ Hash matching: [Known violating content databases, e.g., PhotoDNA, CSAI hashing]
- ☐ Confidence Thresholds:
| Confidence Level | Action | Review Queue |
|---|---|---|
| [>95%] | [Auto-remove, notify user] | [None] |
| [80-95%] | [Hide pending review] | [Priority queue] |
| [60-80%] | [Flag for review, keep visible] | [Standard queue] |
| [<60%] | [No action] | [None] |
- ☐ False Positive Target: [e.g., <2% of auto-removed content overturned on appeal]
- ☐ False Negative Monitoring: [How you detect content the system missed]
- ☐ Model Retraining Cadence: [Weekly, monthly, triggered by policy changes]
Human Review Workflow
- ☐ Review Team Structure:
- ☐ Tier 1 reviewers: [Volume review, clear-cut cases, target decisions per hour]
- ☐ Tier 2 reviewers: [Complex cases, policy edge cases, cultural context]
- ☐ Escalation reviewers: [High-profile accounts, legal-adjacent, PR risk]
- ☐ Queue Prioritization:
| Priority | Criteria | SLA |
|---|---|---|
| P0 - Critical | [Immediate safety risk, legal mandate] | [1 hour] |
| P1 - High | [High-confidence policy violation, user reports with context] | [4 hours] |
| P2 - Standard | [Flagged by automation, single user report] | [24 hours] |
| P3 - Low | [Borderline content, low engagement] | [72 hours] |
- ☐ Reviewer Tooling:
- ☐ Content preview with full context (thread, profile, history)
- ☐ One-click action buttons (remove, restrict, approve, escalate)
- ☐ Policy reference panel with examples
- ☐ Decision audit log
- ☐ Reviewer Wellbeing:
- ☐ Maximum exposure time per shift: [Hours]
- ☐ Content warning system before graphic material
- ☐ Access to mental health support
- ☐ Rotation policy for graphic content queues
User Reporting System
- ☐ Report reasons map directly to Content Policy Tiers
- ☐ Report flow completes in under [X] taps/clicks
- ☐ Reporter receives acknowledgment within [X] minutes
- ☐ Reporter receives outcome notification within [X] hours
- ☐ Bulk reporting and coordinated abuse detection
- ☐ Reporter feedback loop: "We took action" or "We reviewed and it does not violate our policies"
Enforcement Actions
| Violation Severity | First Offense | Second Offense | Third Offense |
|---|---|---|---|
| [Tier 1 - Severe] | [Content removal + warning] | [Temporary suspension (X days)] | [Permanent ban] |
| [Tier 1 - Standard] | [Content removal + warning] | [Content removal + strike] | [Temporary suspension] |
| [Tier 2] | [Content restricted + notification] | [Content removal + warning] | [Content removal + strike] |
- ☐ Strike system: [Number of strikes, decay period, reset conditions]
- ☐ Account-level signals: [What triggers proactive review of an entire account]
- ☐ Shadow restrictions: [If applicable, define scope and transparency policy]
- ☐ Notification templates for each enforcement action
Appeals Process
- ☐ User can appeal any enforcement action within [X] days
- ☐ Appeal reviewed by a different reviewer than the original decision-maker
- ☐ Appeal decision returned within [X] hours/days
- ☐ Maximum [X] appeals per action
- ☐ Escalation path if user disputes the appeal outcome: [Legal team, ombudsperson, external board]
- ☐ Appeal outcome metrics tracked: overturn rate, resolution time, user satisfaction
Metrics and Reporting
| Metric | Target | Cadence |
|---|---|---|
| [Time to first action (P0)] | [<1 hour] | [Daily] |
| [Auto-detection precision] | [>95%] | [Weekly] |
| [Appeal overturn rate] | [<5%] | [Monthly] |
| [User report resolution time (P95)] | [<24 hours] | [Daily] |
| [Reviewer accuracy (inter-rater agreement)] | [>90%] | [Weekly] |
| [Content actioned / total content] | [Tracked, not targeted] | [Monthly] |
- ☐ Transparency report published: [Quarterly/annually]
- ☐ Internal moderation dashboard for leadership review
Open Questions
| # | Question | Owner | Status |
|---|---|---|---|
| 1 | [Unresolved question] | [Name] | Open |
| 2 | [Unresolved question] | [Name] | Open |
| 3 | [Unresolved question] | [Name] | Open |
Filled Example: Community Discussion Platform
Content Policy Tiers
Tier 1: Prohibited (Immediate Removal)
- Hate speech: slurs, dehumanization, or calls for violence against protected groups
- CSAM: any child sexual abuse material (automatic report to NCMEC)
- Doxxing: sharing private personal information without consent
- Credible threats of violence against individuals or groups
Tier 2: Restricted (Review Required)
- Misinformation: health claims contradicting WHO/CDC guidance (label + reduce distribution)
- Graphic violence: combat footage, accident scenes (age-gate + content warning)
- Harassment: targeted, repeated unwanted contact toward another user
Tier 3: Allowed with Conditions
- Adult content: allowed in age-gated communities with content warnings
- Political advertising: allowed with "Paid promotion" label and advertiser disclosure
Automated Detection (Key Metrics)
| Detection Type | Precision | Recall | Volume/Day |
|---|---|---|---|
| Hate speech (text) | 94% | 87% | 12,000 flags |
| Spam | 98% | 95% | 45,000 flags |
| CSAM (hash match) | 99.9% | 99.5% | 200 flags |
| Harassment (text) | 82% | 71% | 8,000 flags |
Human Review SLAs
- P0 (CSAM, credible threats): <30 minutes, 24/7 coverage
- P1 (hate speech, doxxing): <4 hours
- P2 (harassment, misinformation): <24 hours
Team: 14 Tier 1 reviewers, 4 Tier 2 specialists, 2 escalation leads. All reviewers rotate off graphic content queues after 4 hours.
Key Takeaways
- Write your content policy before building any automated systems. The policy defines what the system enforces
- Set confidence thresholds per violation type. Auto-removal should only apply to high-precision, high-harm categories
- Human reviewers handle nuance that automation cannot. Invest in their tooling, training, and wellbeing
- An appeals process is not optional. Users who feel heard are less likely to leave or escalate publicly
- Track moderation metrics rigorously and publish transparency reports to build platform trust
About This Template
Created by: Tim Adair
Last Updated: 3/4/2026
Version: 1.0.0
License: Free for personal and commercial use