F10 -- Narrative Contagion Model

Models how political narratives spread through networks, predicting viral potential and identifying key amplification vectors.

Key Features

• Narrative extraction: Identify embedded narratives, framing techniques, and emotional triggers within political text.
• Virality prediction: Score each narrative's potential for viral spread (0.0 to 1.0).
• Audience targeting analysis: Determine which audience segments a narrative is designed to reach.
• Spread prediction: Model how a narrative would propagate through media and social networks.
• Response recommendations: Generate strategic responses to counter or amplify identified narratives.
• Emotional trigger mapping: Identify the psychological mechanisms driving narrative engagement.

Server Functions

Function	Endpoint	Description
analyze_narrative	narrative/analyze	Extract and analyze narratives from text
list_analyses	narrative/list	List past narrative analyses
get_analysis	narrative/get	Retrieve a specific analysis

Analysis Output

Each narrative identified in the text includes:

• Theme: The core narrative theme
• Framing: How the narrative frames the issue
• Target audience: Intended demographic segment
• Virality score: Predicted viral potential (0.0--1.0)
• Emotional triggers: Psychological drivers (fear, hope, anger, pride, etc.)

The overall analysis includes:

• Spread prediction: Description of how the narratives would propagate
• Recommended responses: Strategic actions the campaign should take

UI Components

• Narrative analyzer (/narrative): Text submission form with analysis results.
• Narrative cards: Visual display of each identified narrative with virality gauges and emotional trigger tags.
• Spread visualization: Network-style diagram showing predicted propagation paths.
• Response panel: Actionable response recommendations.

Database Tables

• narrative_analyses -- input text, extracted narratives (jsonb), spread predictions, recommendations