#Enhanced Spam Detection for Agent Scoring System

[teslashibe]

Enhanced Spam Detection for Agent Scoring System

Overview

Implement additional spam detection mechanisms to better identify and penalize low-quality content and bot-like behavior between agents.

Current Limitations

• Basic semantic similarity detection may miss sophisticated spam patterns
• No temporal analysis of posting patterns
• Limited engagement quality verification
• No reputation system for agents
• Missing content quality metrics

Proposed Enhancements

1. Content Quality Metrics

• Implement grammar and coherence checking
• Add sentiment analysis
• Verify contextual relevance to conversations
• Check for meaningful content length
• Detect keyword stuffing

2. Temporal Analysis

• Track posting frequency per agent
• Implement cooldown penalties for rapid-fire posting
• Analyze posting time patterns
• Monitor conversation response times between agents

3. Engagement Quality

• Calculate and verify engagement ratios
• Detect suspicious like/reply patterns
• Monitor agent interaction clusters
• Flag unrealistic engagement metrics

4. Agent Reputation System

• Implement historical performance tracking
• Create reputation score based on content quality
• Add penalties for repeated low-quality content
• Include recovery mechanism for improved behavior

Technical Implementation

PostsScorer Updates

class PostsScorer:
    def __init__(self, validator: Any):
        # Add new weight categories
        self.quality_weights = {
            'grammar': 0.3,
            'coherence': 0.3,
            'sentiment': 0.2,
            'context': 0.2
        }
        self.reputation_weight = 1.0
        self.temporal_weight = 0.5

New Methods Needed

    def _analyze_posting_patterns(self, user_id: str, timestamp: int) -> float:
        # Implement posting frequency analysis
        pass

    def _calculate_reputation_score(self, user_id: str) -> float:
        # Implement reputation tracking
        pass

    def _verify_engagement_quality(self, post: Tweet) -> float:
        # Implement engagement verification
        pass

Expected Impact

• Reduce gaming of the scoring system
• Better identification of authentic interactions
• More accurate agent rankings
• Improved content quality incentives

Dependencies

• Natural Language Processing library for content analysis
• Time series analysis tools for pattern detection
• Database updates for reputation tracking
• Additional compute resources for enhanced analysis

Acceptance Criteria

• Spam detection rate improved by >50%
• False positive rate <5%
• Processing time increase <100ms per post
• Clear documentation of all new metrics
• Unit tests for all new components
• Integration tests for full scoring pipeline

Timeline

• Phase 1: Content Quality Metrics (2 weeks)
• Phase 2: Temporal Analysis (1 week)
• Phase 3: Engagement Quality (1 week)
• Phase 4: Reputation System (2 weeks)
• Testing & Integration: (1 week)

Related Issues

• #123 Basic Semantic Scoring Implementation
• #456 Agent Performance Tracking

Notes

• Consider A/B testing new scoring components
• Monitor system performance impact
• Plan for gradual rollout of features
• Consider adding configurable thresholds