Content Moderation Policy and System Specification Template

Start with your daily volume of flagged content (automated flags + user reports) and your target SLA. If you flag 5,000 items per day and a Tier 1 reviewer handles 80 decisions per hour, you need approximately 8 reviewer-hours per day for standard content. Add capacity for P0 coverage (24/7 if your platform requires it) and buffer for spikes. Most platforms underestimate reviewer needs by 30-50% in their first year.

Auto-remove only when your model's precision exceeds 95% for that violation type and the content poses immediate harm (CSAM, credible threats). For everything else, hide pending review. Auto-removal at lower confidence thresholds frustrates legitimate users and erodes trust in the platform. Track your auto-removal overturn rate and adjust thresholds monthly.

Each language needs its own classifier or a multilingual model validated per language. Do not assume an English-trained model generalizes to other languages. Prioritize languages by user volume. For languages where you lack automated coverage, increase human reviewer staffing and use community-based reporting as your primary signal. Clearly document which languages have automated coverage and which rely on human review.

At minimum: total content actioned (broken down by policy category), enforcement actions taken (removals, warnings, suspensions, bans), appeal volumes and overturn rates, government requests received and complied with, and automated vs. human review split. Publish quarterly or semi-annually. Use the [metrics framework](/glossary/north-star-framework) to identify which moderation metrics matter most to your stakeholders.

Write your content policy to be as specific as possible. Vague rules like "no inappropriate content" force reviewers to make subjective calls, which leads to inconsistency. Instead, define exactly what constitutes a violation with concrete examples for each category. Build graduated enforcement (warning before suspension before ban) so minor infractions do not receive disproportionate responses. Give users clear explanations of why their content was actioned and a meaningful appeals path. ---