How It Works

Truth isn't binary. Neither is our analysis.

Our Approach

Facts + Framing + Context

01

Probabilistic, Not Binary

We don't say "TRUE" or "FALSE." We calculate Bayesian probabilities (0-100%) based on evidence strength, source quality, and context.

02

Framing Matters

Facts can be accurate yet misleading. We analyze both veracity AND presentation— what's said, what's omitted, and what you're meant to believe.

03

Full Transparency

Every analysis shows our work: evidence sources, Bayesian calculations, detected biases. No black box.

The Process

Seven-Stage Analysis

Powered by Grok-4 • Algorithm v4.0

01

Content Extraction

Extract and chunk content for analysis. Handles unlimited article length while maintaining context.

02

Claim Extraction

Identify verifiable claims. Classify as factual, predictive, opinion, or emotive.

03

Bayesian Evidence Analysis

Calculate probabilities using Bayes' theorem. Prior probability + evidence quality = posterior probability.

04

Framing Detection

Detect manipulation: temporal framing, omissions, causal claims, scale manipulation, and psychological exploitation.

05

Author Credibility Research

Research author background, expertise, track record, and historical accuracy patterns.

06

Dual Credibility Scoring

Two scores: Factual Credibility (claim accuracy) and Presentation Credibility (framing quality).

07

Truth-Seeking vs Tribe-Signaling

Bayesian analysis on the Truth-Seeking ↔ Tribe-Signaling spectrum. Does content expand discourse or inflame tribal identity?

Why Bayesian?

Because truth is probabilistic

Traditional fact-checkers use binary labels (TRUE/FALSE). We use probabilities (0-100%). Why? Because most claims aren't absolutely certain—they're supported by evidence of varying quality.

❌ Traditional

  • Binary labels (TRUE/FALSE)
  • No uncertainty shown
  • Ignores base rates
  • Misses framing tactics

✅ TruthSignal

  • Probability scores (0-100%)
  • Shows confidence levels
  • Uses priors and context
  • Detects manipulation
Framing Detection

The Hidden Manipulation

Most misinformation isn't false. It's true facts with deceptive framing.

Temporal Framing

Old events as breaking news

🍒

Omission Framing

Cherry-picked facts, hidden context

🔗

Causal Framing

Correlation as causation

📏

Scale Manipulation

Misleading percentages and units

🧠

Psychological Tactics

Exploiting emotions over logic

⚖️

False Balance

Fringe views as equally valid

v4.0

Current Algorithm

October 18, 2025

Seven-stage analysis with Bayesian Truth-Seeking ↔ Tribe-Signaling spectrum. Includes historical framing across six decades, Overton window tracking, and intellectual interest assessment.

See it in action →

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