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Real-time automated analysis of live video frames & audio to surface or auto-enforce against policy violations with minimal latency. Use this guide to enable, configure thresholds, act on incidents, and continuously tune signal quality.

Enable the Feature

1

Request Access

Contact support to enable livestream moderation.
2

Provisioning

Support confirms activation; feature appears in Console settings.
3

Shadow Phase (Recommended)

First 1–2 weeks: log results but keep automatic termination conservative.

Enable

Provision & price

Configure

Per-category thresholds

Monitor

Status & incident volume

Intervene

Stop / Delete streams

Tune

Threshold calibration

Audit

Review false positives

How Detection Works

The system continuously samples frames (and audio signals where applicable) and classifies content into risk categories. Each detection produces category confidence scores (0–100%). Your configured thresholds decide whether a stream:
OutcomeTriggerSystem ActionUser Experience
PassAll confidences below Flagged thresholdsContinuesNormal playback
FlaggedAny category ≥ Flagged threshold but < TerminatedMarked for review (no auto-stop)Appears normal to viewers
TerminatedAny category ≥ Terminated thresholdBroadcast stopped; post soft‑deletedAppears as normally ended

Detection Categories

Explicit sexual imagery/acts. Maintain strict thresholds to minimize exposure risk.
Graphic violence or severe physical harm. Sensitivity may vary by regional norms.
Illegal / compliance restricted material (e.g., weapons, contraband). Low tolerance.
Borderline or contextual material. Often noisier—calibrate carefully to reduce review overload.
Offensive language or slurs (audio/overlays). Tune to community standards.

Configuring Confidence Thresholds

  • Concepts
  • Defaults
  • Precision vs Recall
  • Calibration Cycle
  • Record Keeping
Two thresholds per category: Flagged (human review) and Terminated (auto-stop). Flagged <= Terminated must always hold.
A threshold of 0 (or extremely low) will flood the queue with low-confidence matches, overwhelming moderators and increasing costs.

Tuning Playbook

Flag queue FPR > 30% or moderator backlog growing & SLA breach risk.
Confirmed late detections or missed early intervention opportunities.
Any unjustified auto-termination (precision drop) or creator complaints validated.
Severe violations frequently only reaching Flagged state; evidence of slow manual response.
Temporarily widen only the lowest-signal category (e.g., Inappropriate) while holding strict categories steady.

Acting on Incidents

  • Flag Review Workflow
  • Stop
  • Delete
  • Escalate
  • Threshold Feedback Loop
Open flagged stream → verify frame samples → Decide: Stop, Delete, or Allow.

Key Metrics

MetricDefinitionTarget / SignalAction Trigger
Flag Volume per HourFlagged streams / hourBaseline after Week 1Sustained spike → investigate attack/content trend
False Positive Rate (Flagged)(Flags cleared as OK) / Flags reviewed< 30%Higher → raise Flagged threshold
Termination PrecisionValid terminations / Total terminations> 95%Drop → raise Terminated threshold & audit samples
Miss IncidentsConfirmed violations not flagged/terminated0 critical missesAny miss → lower relevant thresholds or escalate model gap
Avg Review TimeTime from flag to moderator decisionWithin SLA (e.g., < 5 min)Rising trend → staffing or threshold adjustment
Category Distribution% flags by categoryStable patternSudden skew → targeted abuse or threshold imbalance

Best Practices

Run with only Flagged actions (no Terminated) for initial calibration if risk appetite allows.
Adjust one category per cycle to attribute impact.
Retain representative false positives & misses for model feedback.
Provide clear examples per category to align decisions & reduce subjective variance.
Publish community guidelines emphasizing live content do’s & don’ts.

Troubleshooting

IssueLikely CauseResolution
Overloaded Flag QueueFlagged threshold too low / spike eventRaise threshold 5% or add temporary staffing; isolate noisy category
Repeated False TerminationsTerminated threshold aggressive or misclassified scenesIncrease threshold; review sample frames; report to support
Missed Severe ViolationThreshold too high or model blind spotLower thresholds; capture evidence & escalate for model improvement
Cost SurgeIncreased broadcast minutes + high flag rateAudit usage, enforce creator guidelines, optimize thresholds
Inconsistent Moderator DecisionsLack of unified rubricEstablish decision matrix & training refresh
Slow Review TimesUnderstaffing or UI overloadAdd shift coverage; refine filters; raise thresholds temporarily

Next Steps