How the analysis actually works.

Article text is embedded with a sentence-transformers model and compared by cosine similarity to three reference texts describing left, center, and right political discourse. The relative similarity to each reference produces a score: -1.0 (far left) to +1.0 (far right).

• ·Labels: left (≤ -0.4), leans_left (≤ -0.15), center (|score| < 0.15), leans_right (≥ 0.15), right (≥ 0.4).
• ·Articles fall back to "center" if the embedding model is unavailable.
• ·This measures how closely an article resembles polarized political discourse — not the truthfulness of its claims.

The article embedding is compared against eight reference frames — economic, moral, national_security, health, legal, social, technology, environmental. Any frame with similarity above 0.3 is attached to the article.

• ·An article can carry multiple frames; a tax debate is often both "economic" and "legal".
• ·Frames describe how a story is framed, not the position the article takes on it.

A lexicon-based score: the number of positive words minus negative words, divided by the total matched words. Positive words include breakthrough, recovery, victory; negative words include crisis, collapse, threat.

• ·This is a word-frequency ratio, not a neural sentiment model. It is sensitive to wording and does not understand negation ("not great" is not handled).
• ·The lexicons are English-only; see the Language scope section below.

Starts from the source-level factuality rating (0-100, divided by 100, defaulting to 0.6), then applies penalties for hedging language (×1.5 per unit) and loaded language (×2.0 per unit), clamped to a floor of 0.1.

• ·Hedging (might, allegedly, reportedly) and loaded phrasing (unprecedented, catastrophic) lower the estimate.
• ·This is a heuristic estimate of the confidence language in the text — not an independent fact-check.

Reflects the source-level factuality rating divided by 100. Sources without a rating default to 0.5.

The ratio of words from a loaded/emotional vocabulary (disastrous, outrageous, heroic, crucial, and similar) to total words, capped at 1.0.

The ratio of hedging terms to total words, capped at 1.0. Handles single words (might, seems, allegedly) and multi-word phrases (tends to, in some ways).

Articles are matched against 13 signal keyword groups — market-mover, crisis, regulation, earnings, geopolitics, technology, innovation, conflict, election, health, climate, merger, scandal.

• ·Score 40 when a keyword appears in the body, 60 when a keyword appears in the title, 80 when three or more keywords match, capped at 100.
• ·Signal matching is keyword-based; embeddings are used for bias and framing only.

Scans for nine propaganda techniques — name calling, glittering generalities, transfer, testimonial, plain folks, card stacking, bandwagon, fear, and loaded language. Intensity is total matches divided by 20, capped at 1.0.

Scores title and body against a clickbait pattern list ("you won't believe", "this is why", "doctors hate"...). Patterns in the title add 0.15 each, in the body 0.08 each.

• ·Excessive capital letters (> 40% of the title) add 0.1, punctuation emphasis and ellipsis teasers add 0.05 each.
• ·An article is flagged is_clickbait when the score exceeds 0.3.

Scans for six spin techniques — minimization, exaggeration, cherry picking, whataboutism, false balance, and weasel words. Spin index is total matches divided by 15, capped at 1.0.

• ·Slant labels: none (≤ 0.1), low (> 0.1), moderate (> 0.3), high (> 0.6).

A weighted composite of propaganda, clickbait, and spin: propaganda intensity × 0.4, clickbait score × 0.3, spin index × 0.3.