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🏆 User Reputation System

Overview

The reputation system is designed to distinguish quality users from spam bots and improve content quality on the confession platform. It uses multiple factors to calculate a comprehensive reputation score and detect suspicious activity patterns.

🎯 Key Features

Multi-Factor Reputation Scoring

  • Activity Score (25%): Account age, confession count, voting activity, recent engagement
  • Quality Score (35%): Average votes received, positive vote ratio, comment engagement, content length
  • Trust Score (40%): Report history, account verification, consistent activity, suspicious pattern detection

Bot Detection Algorithm

  • Activity Rate Analysis: Detects rapid posting patterns
  • Content Repetition: Identifies duplicate or similar content
  • Timing Patterns: Detects unnatural posting intervals
  • Engagement Analysis: Flags accounts with no community interaction
  • Suspicious Patterns: Identifies generic content and short posts

Verification Levels

  • NEW_USER: Default for new accounts
  • VERIFIED: Users with reputation > 200
  • TRUSTED: Users with reputation > 500
  • SUSPICIOUS: Users with bot probability > 70%

📊 Reputation Levels

Level Score Range Description
LEGENDARY 800+ Top contributors, highly trusted
EXPERT 600-799 Experienced, reliable users
TRUSTED 400-599 Verified, active community members
ACTIVE 200-399 Regular contributors
REGULAR 100-199 Established users
NEW 0-99 New or low-activity users

🔧 Technical Implementation

Database Schema

model User {
  // Reputation fields
  reputationScore    Int @default(0)
  activityScore      Int @default(0)
  qualityScore       Int @default(0)
  trustScore         Int @default(0)
  
  // Activity tracking
  lastActivityAt     DateTime?
  dailyConfessionCount Int @default(0)
  dailyVoteCount      Int @default(0)
  dailyCommentCount   Int @default(0)
  
  // Bot detection
  botProbability     Int @default(0)
  isFlagged          Boolean @default(false)
  verificationLevel   String @default("NEW_USER")
  
  // Audit trail
  reputationHistory  ReputationHistory[]
}

model ReputationHistory {
  id              String @id @default(auto()) @map("_id") @db.ObjectId
  userId          String
  changeType      String
  changeAmount    Int
  reason          String
  previousScore   Int
  newScore        Int
  createdAt       DateTime @default(now())
}

Core Functions

calculateUserReputation(userId: string)

Calculates comprehensive reputation score based on:

  • Activity patterns
  • Content quality metrics
  • Community trust indicators
  • Bot detection analysis

monitorUserActivity(userId: string, action: 'confession' | 'vote' | 'comment')

Tracks user activity and triggers reputation recalculation every 10 actions.

detectBotActivity(user: User)

Analyzes user behavior for suspicious patterns:

  • High activity rates (>2 confessions/hour)
  • Repetitive content detection
  • Unnatural timing patterns
  • No engagement flags

🛡️ Bot Detection Features

Activity Rate Monitoring

const activityRate = user.confessions.length / accountAgeHours;
if (activityRate > 2) {
  botScore += 30; // High activity penalty
}

Content Repetition Detection

// Check for duplicate titles and content
const duplicateTitles = titles.filter((title, index) => 
  titles.indexOf(title) !== index
).length;

Timing Pattern Analysis

// Detect too-regular intervals (bot-like behavior)
const regularIntervals = intervals.filter(interval => 
  interval > 5000 && interval < 15000
).length;

Suspicious Pattern Detection

// Check for generic content and short posts
const genericTitles = user.confessions.filter(c => 
  c.title.toLowerCase().includes('test') || 
  c.title.toLowerCase().includes('hello')
).length;

📈 Reputation Calculation

Activity Score (Max 1000 points)

  • Account Age: 1 point per day (max 365)
  • Confession Activity: 5 points per confession
  • Voting Activity: 1 point per vote
  • Comment Activity: 2 points per comment
  • Recent Activity: 3 points per confession in last 7 days

Quality Score (Max 1000 points)

  • Average Votes: 10 points per average vote received
  • Positive Ratio: Up to 200 points for 100% positive votes
  • Comment Engagement: 15 points per average comment received
  • Content Length: Up to 50 points for longer content

Trust Score (Max 300 points)

  • No Reports: 100 points bonus
  • Account Verification: 50 points bonus
  • Consistent Activity: 50 points bonus
  • No Suspicious Patterns: 100 points bonus

🎨 UI Components

UserReputationBadge

Displays user reputation and verification status with tooltips showing detailed metrics.

<UserReputationBadge 
  userId={user.id} 
  showDetails={true} 
/>

Reputation Colors

  • LEGENDARY: Purple
  • EXPERT: Blue
  • TRUSTED: Green
  • ACTIVE: Yellow
  • REGULAR: Orange
  • NEW: Gray

🔄 Integration Points

Automatic Reputation Updates

  • Confession Creation: Triggers activity monitoring
  • Voting: Updates reputation every 10 votes
  • Commenting: Tracks engagement patterns
  • Background Processing: Periodic recalculation

API Endpoints

  • GET /api/user/reputation/[id]: Get user reputation data
  • Integrated with existing confession and voting APIs

🚀 Benefits

For Users

  • Quality Content: Reputation encourages better contributions
  • Community Trust: Users can identify reliable sources
  • Recognition: Achievement system for active contributors

For Platform

  • Spam Prevention: Bot detection reduces low-quality content
  • Content Quality: Reputation incentivizes better posts
  • Community Health: Trust indicators improve user experience

For Moderation

  • Automated Flagging: Suspicious users are automatically flagged
  • Audit Trail: Complete history of reputation changes
  • Risk Assessment: Bot probability helps identify problematic accounts

🔧 Maintenance

Daily Tasks

  • Monitor flagged users for manual review
  • Check reputation distribution for anomalies
  • Review bot detection accuracy

Weekly Tasks

  • Analyze reputation trends
  • Adjust scoring weights if needed
  • Review false positive/negative rates

Monthly Tasks

  • Comprehensive reputation audit
  • Update detection algorithms
  • Performance optimization

📊 Monitoring & Analytics

Key Metrics

  • Average Reputation Score: Platform health indicator
  • Bot Detection Rate: Effectiveness of spam prevention
  • Reputation Distribution: User engagement levels
  • False Positive Rate: Accuracy of bot detection

Alerts

  • High bot probability users (>70%)
  • Rapid reputation changes
  • Suspicious activity patterns
  • System performance issues

🛠️ Future Enhancements

Planned Features

  • Reputation Decay: Scores decrease over inactivity
  • Community Moderation: User-powered reputation adjustments
  • Advanced Bot Detection: Machine learning-based detection
  • Reputation Marketplace: Gamification elements

Potential Improvements

  • Real-time Updates: Live reputation score changes
  • Reputation Challenges: User engagement activities
  • Reputation Transfer: Legacy account benefits
  • Reputation Insurance: Protection against false flags

This reputation system provides a robust foundation for maintaining content quality and community trust while effectively distinguishing between genuine users and automated spam.