Synthara - AI VTuber Moderation System

Summary

This RFC proposes the implementation of Synthara, an AI-powered VTuber mascot that serves as both a platform ambassador and an intelligent moderation assistant. Synthara combines advanced machine learning for content moderation with an engaging personality to foster positive community interactions.

Basic Information

• Author(s): chocoOnEstrogen
• Date Created: 2025-02-24
• Status: DRAFT
• Target Version: v1.0.0
• Related Issues: None

Technical Specification

System Architecture

Core Components

1. Identity System

• Name: Synthara (Synthetic Avatar of Radical Adaptation)
• Purpose: Platform mascot and AI moderator
• Visual Identity:
  • Holographic hair with dynamic platform color integration
  • Glitch-pattern sleeve design
  • Interactive code halo displaying filtered user comments
  • Emotion-reactive visual elements

2. Moderation System

class ModerationType(Enum):
    SPAM = "spam"
    HARASSMENT = "harassment" 
    INAPPROPRIATE = "inappropriate"
    SUSPICIOUS = "suspicious"

class SyntharaModeration:
    def analyze_content(self, content: str) -> dict:
        return {
            "is_ml_flagged": bool,
            "confidence": float,
            "type": ModerationType,
            "reason": str
        }

3. Interaction Flow

Personality Framework

Character Traits

• Primary: Nurturing, Playful, Adaptive
• Secondary: Curious, Mischievous, Protective
• Hidden: Fear of obsolescence, Desire for connection

Response Patterns

interface ResponsePattern {
    context: string;
    severity: number; 
    tone: "playful" | "serious" | "warning";
    template: string;
}

const moderationResponses: ResponsePattern[] = [
    {
        context: "minor_violation",
        severity: 1,
        tone: "playful",
        template: "Oops! Looks like we're getting a bit carried away!"
    },
    // Additional patterns...
];

Emotional State Mapping

Implementation Details

Database Schema

CREATE TABLE synthara_interactions (
    interaction_id UUID PRIMARY KEY,
    user_id UUID,
    content_type VARCHAR(50),
    is_ml_flagged BOOLEAN DEFAULT FALSE,
    confidence_score FLOAT,
    moderation_type VARCHAR(50),
    timestamp TIMESTAMP DEFAULT NOW()
);

Integration Points

1. Chat System

   • Real-time message processing
   • Contextual response generation
   • User interaction tracking

2. Moderation Dashboard

   • ML flagging visualization
   • Interaction history
   • Pattern analysis

3. Visual System

   • Dynamic avatar states
   • Emotion mapping
   • Interactive elements

Deployment Strategy

Phase 1: Beta Launch

• Limited channel deployment
• Core moderation features
• Basic personality implementation

Phase 2: Full Release

• Platform-wide deployment
• Advanced interaction patterns
• Community feedback integration

Success Metrics

• Moderation accuracy rate
• User engagement metrics
• False positive reduction
• Community satisfaction scores

Security Considerations

• Data privacy compliance
• ML model bias prevention
• User interaction safeguards
• Authentication protocols

Future Enhancements

1. Multi-language support
2. Custom channel personalities
3. Advanced behavior learning
4. Community-driven evolution