> ## Documentation Index
> Fetch the complete documentation index at: https://learn.social.plus/llms.txt
> Use this file to discover all available pages before exploring further.
# Overview
> Comprehensive guide to moderating content and managing community safety through the social.plus Console
**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.
Automated text / image / video analysis with confidence tuning
Prioritized queues & human decisions (see AI + queue tuning)
Warnings, suspensions, bans & restriction management
Granular moderator capability & workflow permissions
Raw metrics export for BI & policy tuning
Thresholds, keyword lists, exception & escalation rules
## 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
## 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
## Primary Workflows
**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
**Goal**: Consistent human decisions on queued or escalated items.
1. Moderator claims item from priority queue
2. Context & history surfaced (user, prior actions, report density)
3. Decision applied with required rationale
4. Enforcement + user notification (if applicable)
**Goal**: Leverage community to surface edge cases & emerging abuse.
1. User selects category & optional note
2. Deduplication / credibility weighting
3. Auto-triage (direct queue / auto action / hold)
4. Resolution & optional reporter feedback
**Goal**: Provide due process & policy legitimacy.
1. User submits appeal referencing original action
2. Secondary reviewer (segregated from original)
3. Confirmation or reversal logged
4. Insights looped into calibration
## System Architecture
```mermaid theme={null}
graph TD
A[Content Submission] --> B[AI & Rules Engine]
B -->|Allow| C[Publish]
B -->|Queue| D[Review Queue]
B -->|Block| E[Flag/Delete]
D --> F[Moderator Decision]
E --> F
F -->|Approve| C
F -->|Enforce| G[Clear Flags/Delete]
G --> H[User State & Restrictions]
C --> K[Analytics]
G --> K
```
## Getting Started
Document violation categories & action ladder.
Set confidence thresholds & custom rules.
Ensure user report categories & flows are active.
Assign moderator / supervisor permissions.
Prioritize by severity & workload balance.
Track false positives & SLA compliance.
## 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
## Integration Points
Live events for content status, enforcement & appeals
Programmatic moderation & bulk operations
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.