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Key Benefit: Unified AI + human moderation workflows to proactively detect, review, and resolve policy violations while preserving user trust and transparency.
Maintain a safe and healthy community with social.plus Console’s comprehensive moderation stack. Combine automated detection, structured review queues, user reporting, and analytics-driven enforcement.

AI Detection

Automated text / image / video analysis with confidence tuning

Manual Review

Prioritized queues & human decisions (see AI + queue tuning)

User Actions

Warnings, suspensions, bans & restriction management

Roles & Privileges

Granular moderator capability & workflow permissions

Analytics Export

Raw metrics export for BI & policy tuning

Policy Configuration

Thresholds, keyword lists, exception & escalation rules

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Key Capabilities

  • Low-latency AI screening for text, images, video frames
  • Configurable confidence thresholds (auto allow / queue / block)
  • Custom keyword & regex rule layers
  • Metadata & context-aware scoring (history, reputation)
  • Priority scoring (severity, virality, report density)
  • Workload distribution & claim / assign patterns
  • Batch operations for spam waves
  • Full audit log for every action
  • Category-based reports with optional free-form notes
  • Reporter credibility weighting & duplicate collapse
  • SLA timers & escalation triggers
  • Feedback loop to reporters (accepted / rejected)
  • Action ladder: warn → restrict → suspend → ban
  • Time-boxed penalties & automatic expiry
  • Appeal submission & secondary review layer
  • Consistent policy taxonomy & rationale capture
  • False positive / negative tracking & model tuning inputs
  • Moderator performance & queue aging metrics
  • Violation trend & emerging pattern surfacing
  • Policy effectiveness dashboards

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Moderation Approach

  1. Preventive (AI & rules)
  2. Reactive (user & system reports)
  3. Review (human adjudication)
  4. Appeal (fairness & transparency)
  • Proportionate actions matched to severity
  • Standardized decision templates
  • Calibration sessions & spot audits
  • Transparent communication & appeals

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Primary Workflows

  • AI Screening
  • Manual Review
  • User Reporting
  • Appeals
Goal: Minimize exposure to harmful content before broad distribution.
  1. Content submitted (post / comment / media / stream event)
  2. AI models + rule engine assign risk score
  3. Outcome branch: Allow | Queue | Block
  4. Metadata logged for analytics & tuning

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System Architecture

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Getting Started

1

Define Policies

Document violation categories & action ladder.
2

Configure AI

Set confidence thresholds & custom rules.
3

Enable Reporting

Ensure user report categories & flows are active.
4

Set Roles

Assign moderator / supervisor permissions.
5

Tune Queues

Prioritize by severity & workload balance.
6

Monitor Metrics

Track false positives & SLA compliance.

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Best Practices

  • Calibrate models monthly with sampled decisions
  • Enforce rationale fields on irreversible actions
  • Rotate reviewers for sensitive categories
  • Monitor queue aging; set escalation SLAs
  • Run blind double-review audits
  • Track acceptance / reversal rates per moderator
  • Review appeal overturn patterns
  • Maintain balanced training datasets
  • Start conservative with auto-block thresholds
  • Whitelist benign edge cases iteratively
  • Version & test new rule sets before production
  • Log every automated action with explainability

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Integration Points

Webhook

Live events for content status, enforcement & appeals

API Endpoints

Programmatic moderation & bulk operations

Analytics Export

Raw data for external BI & policy tuning
Compliance: Align enforcement with regional legal requirements (e.g., GDPR, DSA) & retain audit logs for mandated retention periods.