thrillwiki_django_no_react/memory-bank/features/moderation/overview.md

Moderation System Overview

Purpose

The moderation system ensures high-quality, accurate content across the ThrillWiki platform by implementing a structured review process for user-generated content.

Core Components

1. Content Queue Management

• Submission categorization
• Priority assignment
• Review distribution
• Queue monitoring

2. Review Process

• Multi-step verification
• Content validation rules
• Media review workflow
• Quality metrics

3. Moderator Tools

• Review interface
• Action tracking
• Decision history
• Performance metrics

Implementation

Models

# Key models in moderation/models.py
- ModeratedContent
- ModeratorAction
- ContentQueue
- QualityMetric

Workflows

1. Content Submission

   • Content validation
   • Automated checks
   • Queue assignment
   • Submitter notification

2. Review Process

   • Moderator assignment
   • Content evaluation
   • Decision making
   • Action recording

3. Quality Control

   • Metric tracking
   • Performance monitoring
   • Accuracy assessment
   • Review auditing

Integration Points

1. User System

• Submission tracking
• Status notifications
• User reputation
• Appeal process

2. Content Systems

• Parks content
• Ride information
• Review system
• Media handling

3. Analytics

• Quality metrics
• Processing times
• Accuracy rates
• User satisfaction

Business Rules

Content Standards

1. Accuracy Requirements

   • Factual verification
   • Source validation
   • Update frequency
   • Completeness checks

2. Quality Guidelines

   • Writing standards
   • Media requirements
   • Information depth
   • Format compliance

Moderation Rules

1. Review Criteria

   • Content accuracy
   • Quality standards
   • Community guidelines
   • Legal compliance

2. Action Framework

   • Approval process
   • Rejection handling
   • Revision requests
   • Appeals management

Future Enhancements

Planned Improvements

1. Short-term

   • Enhanced automation
   • Improved metrics
   • UI refinements
   • Performance optimization

2. Long-term

   • AI assistance
   • Advanced analytics
   • Workflow automation
   • Community integration

Integration Opportunities

1. Machine Learning

   • Content classification
   • Quality prediction
   • Spam detection
   • Priority assignment

2. Community Features

   • Trusted reviewers
   • Expert validation
   • Community flags
   • Reputation system