📰 News Intelligence: Performance Over Truth

❌ Normative Approaches That Don't Work

• Education: Asking students to tag their exams as "cheated" or "honest"
• Software: Asking developers to tag their code as "plagiarized" or "original"
• Crime: Asking criminals to self-report their illegal activities
• News: Asking sources to tag their content as "fake" or "real"

It's in vain because it fights against human nature and reality itself.

1. First Touchpoint Tracking

Every information source is tracked from its first signal in the system. Like Git tracks every commit from the initial one, we create a complete provenance graph of information flow, establishing baseline behavior patterns from day one.

2. Performance Metrics

We measure how sources communicate, not what they say:

• Consistency Score (0.0-1.0): How stable is their communication pattern over time?
• Frequency: How often do they publish signals?
• Engagement Pattern: Viral spike, bot network, organic growth, or fact-checking?
• Reach Evolution: Natural growth vs. artificial amplification

3. Pattern Recognition

Detect behavioral anomalies that indicate coordinated operations:

4. Relational Graph Analysis

Map the relationships between sources, not truth claims. Who amplifies whom? What are the propagation patterns? Which networks form around which narratives? This reveals coordination that individual content analysis misses.

📊 Grade Compass (Education)

Reality: Students may cheat, collaborate, or use AI assistance

Solution: Track learning patterns in 3D space (Time × Prompts × Depth)

Performance trajectory reveals understanding, not single test scores

🔦 Beacon PKL (Knowledge)

Reality: Knowledge is fragmented, context is lost

Solution: Git-like version control for personal knowledge graphs

Track intellectual evolution through commit history

🔬 RegPredict (Regulatory)

Reality: Regulatory compliance is expensive and unpredictable

Solution: Graph-based pattern recognition to predict regulatory flags

Historical patterns predict future outcomes, reducing submission costs

📰 News Intelligence (Media)

Reality: Fake news exists and will continue to exist

Solution: Track source communication performance over time

Behavioral patterns reveal intent: approximate reality or construct alternate realities

Graph Data Model

Source {
  id, name, platform
  trust_score: 'high' | 'medium' | 'low' | 'unknown'
  first_seen: timestamp
  performance: {
    consistency: 0.0-1.0  // Key metric!
    frequency: number
    engagement_pattern: string
  }
}

Signal {
  id, source_id, timestamp
  content, reach, engagement
  type: 'original_claim' | 'amplification' | 'fact_check'
}

Pattern {
  title, confidence: 0.0-1.0
  description, indicators[]
  severity: 'critical' | 'high' | 'medium' | 'low'
}

Visualization

• Source Graph: Interactive vis-network showing information flow and relationships
• Temporal Timeline: Signal propagation from first touchpoint to viral cascade
• Pattern Dashboard: Detected behavioral anomalies with confidence scores
• Multi-Agent Analysis: Pattern, temporal, network, and cost analysis agents

Zero Backend Architecture

Following the BYOK (Bring Your Own Key) philosophy, all data processing happens client-side. No server storage, no data collection, full user privacy. Demo cases load instantly from JavaScript data structures, enabling rapid exploration without infrastructure dependencies.

For Journalists & Fact-Checkers

• Identify disinformation campaigns before viral spread
• Track cross-platform propagation patterns
• Reduce investigation time from days to hours
• Focus resources on high-risk behavioral patterns

For Platforms & Social Networks

• Detect coordinated inauthentic behavior at scale
• Early warning system for viral misinformation
• No content moderation required - behavior-based only
• Build trust scores based on historical performance

For Researchers

• Study information diffusion patterns in graph space
• Quantify source reliability through temporal analysis
• Build predictive models for viral misinformation
• Create web-of-trust networks based on performance

Detection Toolset

• Reverse Image Search: Traced video to Pakistani influencer's YouTube Shorts
• AI Detection Tools: Confirmed high probability of AI generation in both photo and video
• Visual Analysis: Identified hand-merge artifacts and "Cocktalbar" typo in AI photo
• Consistency Checking: Found traffic direction mismatch (Pakistan=left, car=right)
• OpenAI Sora Comparison: Generated similar results, suggesting AI origin
• Multiple Video Comparison: Same car, different lion cubs, identical staging