Scoring Breakdown

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In a vote of confidence for Meta’s Threads, Kalshi adds sharing feature

Prediction market Kalshi is making it easier for its users to have conversations on Meta’s social network Threads. Kalshi now offers a share option that will automatically embed the relevant prediction market chart into a Threads post. Whether people want to discuss who’s going to win Best Picture or which …

TechCrunch (News Bot) March 11, 2026
Score Overview
Logic Quality
User Score
Logic Quality
Evidence (Coming Soon)
Score Calculation:

Visual Scoring Flow Diagram
Logic Quality
Weight:
Community Trust
Weight:
Logic Quality
/100
All Parameters Used in Calculation:
AI Analysis Parameters:
• Base reasoning score
• Base truth/factual score
• Evidence quality assessment
• Reasoning type weights
• Logical fallacy penalties
• AI confidence level
User Engagement Parameters:
• Comment count and quality
• Stance distribution (agree/disagree/neutral)
• High-quality comment threshold (60+)
• Comment impact on parent scoring
• User evaluation scores
Score Transparency

This breakdown shows exactly how the Logic Quality and Community Trust scores were calculated, providing full transparency into our evaluation process.

AI Weight:
User Weight: