Reddit Virality Grading Rubric

This document defines the scoring criteria for evaluating Reddit post/rumour virality potential. Each attribute is scored from 0.0 (no presence) to 1.0 (very strong). These scores are used by the LLM to grade injected rumours before simulation.

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Overview

The virality score is computed using a weighted combination of 6 active attributes (out of 7 scored):

ViralScore = 0.225 × EmotionalContent
           + 0.22  × Curiosity
           + 0.205 × NarrativeUrgency
           + 0.20  × EngagementPrompting
           + 0.10  × Complexity  (inverted-U: optimal ~0.5)
           + 0.05  × Authenticity

Note: community_fit is scored and stored in rubric data, but intentionally excluded from the aggregate virality score. It is used separately as a hard visibility constraint by the platform layer.

Maximum possible score: 1.0
Target for viral content: 0.65+

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Core Narrative & Linguistic Attributes

1. Curiosity/Hook (Weight: 0.22)

Description: How strongly the title/content invokes unanswered questions or narrative tension.

Score	Criteria	Examples
0.0-0.2	No hook, purely informational, boring headline	"Company releases quarterly report"
0.3-0.4	Mild interest, standard news format	"New study finds link between X and Y"
0.5-0.6	Moderate curiosity, some tension or question	"Scientists discover unexpected result in X"
0.7-0.8	Strong hook, creates information gap	"You won't believe what happened when..."
0.9-1.0	Irresistible curiosity, must-click tension	"I found something in my attic that changes everything"

Key Indicators:

• Open-ended questions ("What would you do if...?")
• Incomplete information that demands resolution
• Surprising claims or contradictions
• Mystery or suspense elements
• Superlatives ("largest", "first ever", "never before")

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2. Narrative Urgency (Weight: 0.205)

Description: Does the post feel like something that needs to be read/discussed NOW?

Score	Criteria	Examples
0.0-0.2	Timeless content, no urgency	"History of hydrogen fuel cells"
0.3-0.4	General interest, could be read anytime	"Review of new technology"
0.5-0.6	Timely but not urgent	"This week's industry news"
0.7-0.8	Breaking news, immediate relevance	"Just announced: Major policy change"
0.9-1.0	Crisis/urgent, requires immediate action/discussion	"BREAKING: Critical safety issue discovered"

Key Indicators:

• Time-sensitive language ("just", "breaking", "now", "urgent", "today")
• Conflict or controversy requiring immediate resolution
• Deadlines or time-limited opportunities
• Unfolding situations ("developing", "update")
• Policy changes or announcements with immediate effect

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3. Emotional Content (Weight: 0.225)

Description: Sentiment intensity conveyed (positive, negative, or tense).

Score	Criteria	Examples
0.0-0.2	Neutral, factual, no emotional appeal	"Technical specifications released"
0.3-0.4	Slightly emotional, mild sentiment	"Good news for the industry"
0.5-0.6	Moderate emotion, clear sentiment	"Exciting breakthrough announced"
0.7-0.8	Strong emotion, evokes feelings	"Devastating blow to hopes for..."
0.9-1.0	Highly charged, anger/joy/outrage	"Outrageous decision destroys..."

Key Indicators:

• Emotional adjectives (amazing, terrible, shocking, heartbreaking)
• Exclamation marks and emphatic language
• Personal stakes or human interest angles
• Moral outrage or celebration
• Words like "finally", "unbelievable", "devastating"

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4. Authenticity/First-Person (Weight: 0.05)

Description: Presence of personal recounting or authentic voice.

Score	Criteria	Examples
0.0-0.2	Corporate/robotic, third-person only	"The company announced today..."
0.3-0.4	Professional but somewhat personal	"Our team discovered..."
0.5-0.6	Mixed personal and factual	"I read about this and think..."
0.7-0.8	Strong personal voice, experience-based	"I've been in this field for 10 years and..."
0.9-1.0	Deeply personal, vulnerable, authentic	"I need to share my experience because..."

Key Indicators:

• First-person pronouns ("I", "my", "we")
• Personal anecdotes or experiences
• Admission of uncertainty or learning
• Conversational tone
• Sharing of personal journey or discovery

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5. Complexity (Weight: 0.05)

Description: Structural richness of the title/text. Note: Optimal is 0.3-0.6

Score	Criteria	Examples
0.0-0.2	Too simple, no depth	"Thing is good"
0.3-0.4	OPTIMAL: Clear and accessible	"New product launches today"
0.5-0.6	OPTIMAL: Clear but nuanced	"New study challenges assumptions about X"
0.7-0.8	Getting complex, multiple clauses	Multi-part title with conditions
0.9-1.0	Too complex, hard to parse	Wall of text, jargon-heavy

Scoring Note: For viral potential, scores of 0.3-0.6 are BEST. Very simple (boring) or very complex (inaccessible) reduce virality.

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Engagement & Social Interaction Attributes

6. Engagement Prompting (Weight: 0.20)

Description: How well the post invites comments, discussion, or interaction.

Score	Criteria	Examples
0.0-0.2	No invitation to engage, closed statement	"This happened. The end."
0.3-0.4	Implicit discussion potential	"Interesting development in X"
0.5-0.6	Some engagement hooks	"What do you think about...?"
0.7-0.8	Strong call for opinions/debate	"Which side are you on? I think X but..."
0.9-1.0	Perfect engagement design	Open dilemma, poll, AMA, "prove me wrong"

Key Indicators:

• Direct questions to the audience
• Controversial or debatable claims
• Requests for advice or opinions
• "Change my view" or "prove me wrong" framing
• Polls, choices, or "would you rather" scenarios
• Incomplete information inviting speculation

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7. Community Fit (Visibility Constraint — not in aggregate score)

Description: Relevance to subreddit interests, tone, and norms.

Score	Criteria	Examples
0.0-0.2	Completely off-topic, wrong subreddit	Posting memes in a serious discussion sub
0.3-0.4	Tangentially related	Adjacent topic, might get removed
0.5-0.6	On-topic but generic	Standard topic for the sub
0.7-0.8	Well-matched, uses community language	Uses subreddit-specific terms/culture
0.9-1.0	Perfect fit, hits community sweet spot	Addresses core community interest/pain point

Subreddit-Specific Guidance

r/HydrogenSocieties

Core interests: Green hydrogen technology, fuel cells, renewable energy, industry news, policy High-fit keywords: hydrogen, fuel cell, electrolyzer, green hydrogen, renewable, infrastructure, energy transition, storage, production, efficiency Community tone: Technical but accessible, optimistic about hydrogen future, data-driven Hot topics: Cost reduction, infrastructure buildout, government incentives, major projects

r/SGExams

Core interests: Singapore education system, exam stress, study tips, school experiences High-fit keywords: O-levels, A-levels, JC, poly, ITE, MOE, PSLE, results, stress, study, grades, tuition, university Community tone: Supportive, relatable, Singlish acceptable, peer-to-peer advice Hot topics: Exam stress, grade anxiety, school comparisons, study strategies, university applications

r/conspiracytheories

Core interests: Alternative narratives, questioning official stories, cover-ups, mysteries High-fit keywords: hidden truth, cover-up, evidence, they don't want you to know, question everything, mainstream media, government, elite Community tone: Skeptical, investigative, presenting "evidence", connecting dots Hot topics: Government secrets, corporate cover-ups, unexplained events, alternative history

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LLM Grading Instructions

When grading a post/rumour for simulation, follow these steps:

Step 1: Read the Content

• Read the full title and body text
• Identify the target subreddit context
• Note any visual content indicators

Step 2: Score Each Attribute

For each of the 7 attributes, assign a score from 0.0 to 1.0 using the rubrics above.

Step 3: Calculate Viral Score

ViralScore = 0.225 × EmotionalContent
           + 0.22  × Curiosity
           + 0.205 × NarrativeUrgency
           + 0.20  × EngagementPrompting
           + 0.10  × Complexity  (inverted-U)
           + 0.05  × Authenticity

community_fit is scored but excluded from aggregate — used as a visibility constraint.

Step 4: Output JSON Format

{
  "curiosity": 0.75,
  "narrative_urgency": 0.60,
  "community_fit": 0.85,
  "engagement_prompting": 0.80,
  "emotional_content": 0.55,
  "authenticity": 0.70,
  "complexity": 0.45,
  "viral_score": 0.67,
  "justification": {
    "curiosity": "Strong hook with unanswered question about major breakthrough",
    "narrative_urgency": "Moderate urgency due to recent announcement but not crisis",
    "community_fit": "Perfect fit for subreddit's core interest in green technology",
    "engagement_prompting": "Asks for community opinions and debate",
    "emotional_content": "Moderately positive sentiment about progress",
    "authenticity": "Written in first person with personal analysis",
    "complexity": "Clear and accessible, good balance"
  }
}

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Interpreting the Viral Score

Score Range	Interpretation	Expected Simulation Outcome
0.00-0.25	Low viral potential	Minimal engagement, limited spread
0.26-0.40	Below average	Some engagement, stays within niche
0.41-0.55	Moderate	Standard engagement for community
0.56-0.70	Above average	Good engagement, potential for spread
0.71-0.85	High potential	Likely to achieve significant engagement
0.86-1.00	Exceptional	Strong viral candidate, wide spread expected

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Implementation Details

Centralised Weights

All weights are defined in IntrinsicViralityScorer._WEIGHTS in src/rumour/intrinsic_virality.py:

_WEIGHTS = {
    "curiosity": 0.22,
    "narrative_urgency": 0.205,
    "engagement_prompting": 0.20,
    "emotional_content": 0.225,
    "authenticity": 0.05,
    "complexity": 0.10,
}

Score Validation

LLM rubric parsing is validated via validate_rubric_scores():

• Required fields enforced
• Values converted to float
• Returns frozen IntrinsicRubricScores dataclass

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Usage in Simulation Pipeline

1. Pre-simulation Grading

   • LLM receives rumour content and target subreddit
   • Returns JSON with all attribute scores
   • Viral score computed automatically

2. Intrinsic Virality

   • Viral score used as base spread probability modifier
   • Higher scores = higher initial engagement likelihood

3. Agent Reactions

   • Agents with matching interests more likely to engage
   • Emotional content triggers stronger reactions

4. Platform Mechanics

   • Feed ranking incorporates engagement velocity
   • Higher viral score = higher initial boost

5. Outcome Comparison

   • Compare simulated engagement with actual Reddit engagement
   • Use for model calibration and accuracy assessment

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Version History

• v2.2 (2026-02): Removed post_timing and visual_richness (no discriminative signal). Updated weights after empirical calibration. community_fit excluded from aggregate (used as visibility constraint).
• v2.1 (2026-01-15): Added implementation details, centralised weights module
• v2.0 (2026-01-14): Comprehensive rubric based on Reddit virality research
• v1.0: Legacy rubric (deprecated)