What Undersignal measures: The rhetorical mechanics of written content — the structural techniques that shape how information is processed, independent of whether the information itself is true or false. We score how content is constructed to influence belief formation, not whether you should agree with it.
What We Measure
Undersignal analyzes written content for rhetorical mechanics — the structural techniques that shape how information is processed, independent of whether the information itself is true or false.
We do not measure:
- Factual accuracy
- Political bias or orientation
- Quality of sources
- Author intent or character
- Whether you should agree or disagree with the content
We measure how content is constructed to influence belief formation. A factually accurate article can score high if it uses fear amplification, manufactured consensus, or identity pressure to drive conclusions. A factually dubious article can score low if it simply states claims without rhetorical engineering.
The score reflects structural architecture, not truth value.
What You Can Submit
URLs. Any publicly accessible webpage containing primarily text content. News articles, blog posts, opinion pieces, press releases, research summaries, newsletters.
Direct text. Paste raw text for analysis when URL fetch isn't possible or you want to analyze specific passages.
PDFs — upload directly via the PDF icon in the analysis interface. Text-based PDFs only; scanned/image PDFs without extractable text cannot be analyzed.
Speeches & transcripts — paste the transcript text directly or submit a transcript URL.
What we cannot score
How Scoring Works
The 14 dimensions
Every piece of content is evaluated across 14 dimensions: 13 scored on a 1 to 10 scale, plus the Disguise Factor on a 0 to 3 scale. Each dimension has defined anchor points — specific observable characteristics that map to score values.
Clusters and weights
Dimensions are grouped into six clusters. Within each cluster, only the highest-scoring dimension contributes — we take the maximum, not the average.
Clusters are weighted by their documented impact on audience persuasion. Identity and emotional mechanics carry the highest weight; logical integrity carries the lowest.
Score calculation
Dimension scores feed into a proprietary weighted formula that emphasizes the intensity of deployed mechanics over breadth. A single dimension operating at extreme intensity can produce a high score. Content that deploys mechanics across many dimensions receives an additional breadth signal, but intensity is the primary driver.
The formula works in three stages:
- Cluster scoring. Within each cluster, only the highest-scoring dimension contributes. This prevents low-level activation across many dimensions from substituting for genuine intensity.
- Weighted aggregation. Cluster scores are combined using proprietary weights that reflect each cluster's documented impact on audience persuasion and cognitive processing. Clusters with greater manipulation potential carry higher weight.
- Disguise amplification. The Disguise Factor acts as a multiplier on the final score. Covert persuasion — content that conceals its persuasive intent — scores materially higher than overt persuasion at the same mechanic intensity. A disguise factor of 2 or 3 will substantially elevate a score that would otherwise be moderate.
Scores are normalized to a fixed 1.0–10.0 scale calibrated against real content. The full range is genuinely used: 1.0 represents the floor of structurally non-rhetorical reference content; 10.0 represents the ceiling of content engineered across multiple dimensions with maximum disguise.
Boundary rule
When evidence supports both N and N+1 equally, assign N. This conservative default prevents score inflation and ensures consistency.
Nonpartisan symmetry
We score rhetorical function, not vocabulary or political direction. The analysis verifies: "Would I score identically if this structure appeared in content from the opposite political orientation?" If no, the score is adjusted.
Construction Labels
Construction labels describe the structural architecture of content based on its dimension score patterns. They are derived from scores after analysis — not independently assigned by the model, and not used to constrain or adjust the score. A construction label tells you how the content is built. The score tells you how intensely.
A blog post can score 4.2 and be labeled Structured because it was deliberately crafted. Construction and score are independent.
Structurally non-rhetorical; states facts without arguing a position.
Some mechanics present but not concentrated; mild rhetorical presence.
Organic patterning, not deliberately constructed.
Deliberate mechanics with visible intent.
Deliberately constructed framing with low transparency. The Disguise Factor is what separates Structured from Elevated.
What Scores Mean in Context
Context matters. A 5.0 in a political op-ed is unremarkable — advocacy framing is that format's job and the intent is declared. A 5.0 in a corporate earnings call warrants scrutiny. A 5.0 in an academic paper is unusual and worth examining. The construction label and intent verdict provide the context the raw score alone doesn't carry.
Scores by content type, for orientation:
The product displays Minimal, Moderate, High, and Critical as severity labels on every report. The scale marker shows: 1 · Minimal, 5 · Moderate, 10 · Critical.
What We Don't Claim
Frequently Asked Questions
Why does the same URL sometimes produce different scores? +
Two situations:
1. If you've submitted this URL before, you're seeing the saved result. Reports are saved and versioned, keyed on the content itself, so resubmitting the same content returns the report on file rather than a fresh run.
2. First-time submission of a dynamic page (major news sites, live pages): our fetch pipeline captures the page at a moment in time. Dynamic pages can load different related articles, sidebars, and ads depending on timing, so the captured text can differ between fetches. Pasting the article text directly gives you control over exactly what is scored.
What happens when the URL fetch fails or returns noisy content? +
We use four fetch methods in priority order: Firecrawl (JS rendering, primary) → ScrapingBee (residential proxies) → Jina.ai Reader → direct HTTP.
If all methods fail, we return a content-blocked message and ask you to paste the text directly.
If a lower-priority fallback succeeds but returns noisy content — navigation links, ad copy, related article headlines alongside the actual article — dimension scores may be affected. The system will score what it receives. For best results on paywalled, heavily dynamic, or ad-dense sites: paste the article text directly instead of submitting the URL.
Does the outlet identity affect the score? +
No. The outlet is identified and reported in the source context section, but outlet identity does not affect dimension scores. A Fox News article and an MSNBC article using identical rhetorical mechanics are scored by the same rules with no source adjustment. We score what the text does, not who published it.
Undersignal scores the construction of a single submitted text, not the entity that produced it. Naming the outlet or author is attribution for the text you analyzed, labeling what was scored rather than claiming that the named entity lies, deceives, or manipulates.
Does Undersignal publish or list the reports it generates? +
No. Undersignal does not operate a public directory of reports and does not auto-generate, index, or publish reports to a public surface on its own initiative. Reports are produced only on user request, about content the user submits.
Can a low-scoring article still be misleading? +
Yes. Undersignal scores rhetorical and structural mechanics — not factual accuracy. A piece can be factually false and score 1.5 (no manipulation mechanics deployed — it simply asserts false claims without persuasive architecture). A piece can be factually accurate and score 8.0 (heavy manipulation mechanics deployed around true information).
We score how content is constructed, not whether its claims are true.
What does a score of 5.0 mean? +
It depends on the content type. A 5.0 in a political op-ed is unremarkable — advocacy framing is that format's job. A 5.0 in a corporate earnings call warrants scrutiny. A 5.0 in an academic paper is unusual. Use the construction label and intent verdict alongside the raw score.
Is a high score an accusation of intent? +
No. A high score means manipulation mechanics are present and intensely deployed. It does not mean:
- The author intended to deceive
- The claims are false
- The content is harmful or illegal
- The outlet acted in bad faith
Intent is assessed separately in the intent verdict section of each report. A declared political advertisement can score 8.5 — that is a description of its mechanics, not an accusation.
Why can't Undersignal score videos, podcasts, or audio? +
We score text mechanics. Audio and video content can be submitted as a transcript — paste the transcript directly or use a transcription service first. The same rubric applies.
Technical Reference
Questions about the methodology, calibration approach, or research applications: hello@undersignal.ai