The moment Donald Trump blew the midterms

What was read

3198 words in the outlet's own voice, plus 7 in the headline. 6% of the text is in quotes, with 3 listed words inside quotes. 5 attribution markers (such as "said" or "according to") found.

Listed words inside quotes (not scored; quoting is reporting, but which quotes to use is an editorial choice): amazing, rigged, massive.

Scare quotes (a phrase of one to three words in quotation marks with no speaker in the same sentence): establishment, rigged.. Since method 3.1.0 their words count as the outlet's own.

Words found

Word choice: obviously 2 points Editorializing
it was really quite stable. Obviously , it was highest at the beginning
actually 1 point Editorializing
Today, among 2026 voters, Democrats actually have the trust advantage on the
obviously attributed to a speaker, not counted Editorializing
I was in my 20s. Obviously , I would have been shocked in
obviously 2 points Editorializing
made much of a difference. Obviously money does matter. Ads do work.
incredible 2 points Editorializing
— you can really do incredible things, while your opponents are wasting
obviously 2 points Editorializing
figure is 15 percent. And obviously , part of that is NIMBY concerns.
rigged 2 points Contentious label
Recession. Ask people, people say rigged . Ask, People overwhelmingly say no. And
actually 1 point Editorializing
happen, they will have to actually pressure Congress to meaningfully protect the
Raises the temperature: very 1 point Raises the temperature
And it seems like a very large portion of that was due
huge attributed to a speaker, not counted Raises the temperature
there was a — a huge spike in youth turnout — which
enormous 1 point Raises the temperature
and Texas, there was an enormous increase in youth turnout and actual
very 1 point Raises the temperature
a lot of people became very nihilistic. They were like, But then,
destroyed 1 point Raises the temperature
up. But the Iran war destroyed it. And he went from being
massive 1 point Raises the temperature
healthcare shutdown corresponded with a massive increase in the salience of healthcare
massive 1 point Raises the temperature
unpopular war while also doing massive tariffs and making a bunch of
backlash 1 point Raises the temperature
— Trump simply induced a backlash among specific groups that are more
massive 1 point Raises the temperature
from Tuesday night was the massive backlash to data center construction. Forty
backlash 1 point Raises the temperature
Tuesday night was the massive backlash to data center construction. Forty out
very 1 point Raises the temperature
say no. And these are very big changes from before the pandemic.
very 1 point Raises the temperature
of voters say it’s either very likely or somewhat likely that there
very 1 point Raises the temperature
64 percent say it’s either very likely or somewhat likely that AI

Layer scores

Rates are counted points per 1,000 words of the story's own text. For a story longer than 200 words, the rate used is the average of the whole-story rate and the rate in its densest 200-word stretch, so neutral filler cannot dilute a loaded passage away. Score = 100 × (loaded end − rate used) ÷ loaded end, kept between 0 and 100. Headline words count only in the headline layer.

Layer	Points	Words	Whole-story rate	Densest 200 words	Rate used	Scale	Score
Word choice	12	3198	3.8	20.0 (words 2851 to 3050)	11.9	0 to 40	70
Temperature	12	3198	3.8	15.0 (words 2901 to 3100)	9.4	0 to 40	77
Flagged attribution phrases	0	3198	0.0	0.0 (words 1 to 200)	0.0	0 to 20	100
Headline wording	0	25	0.0	headline only	0.0	0 to 240	100

Densest stretch: Word choice, words 2851 to 3050 of 3198, with obviously, rigged; Temperature, words 2901 to 3100 of 3198, with very, very, very.

Shown, not scored: the headline's rate minus the rate of the story's first 80 words is 0.0 points per 1,000 words. Experimental: 1 of 1 names and figures in the headline also appear in the text read.

Overall

Weighted geometric mean of the layers that are established, with 10 added to each layer first and taken away at the end: (Word choice 70 + 10)^0.4 × (Temperature 77 + 10)^0.3 × (Flagged attribution phrases 100 + 10)^0.2 × (Headline wording 100 + 10)^0.1 − 10 = 80. Adding 10 means one layer at 0 lowers the total steeply without collapsing it. A weak layer pulls the total down.

Lowered the score most: Word choice.

Name-swap test: with party, politician and institution names swapped, this report's score moves by 0.0 points.

What a reader could check

People named as sources	0
Unnamed sources	0
Links to primary records (government, court, filings, PDFs)	0
Documents cited in the text (a report, bill, ruling, filing)	1
Checkable specifics (numbers and dates)	64
Data shown (a table or chart)	no
Correction or clarification noted	no

What a reader could check, counted by published patterns. Shown beside the score, never part of it. How each count works

Checked against the official record

No vote count or bill number found in the text read.

Record of this measurement

Method 3.1.0, word-list fingerprint 807a17189402a416, read Thu, Nov 5, 1:58 PM ET by text extractor version 4. Fingerprint of the text read: 7009e5d404a3d850. Internet Archive copy nearest the time it was read. The text read was not kept (read before method 3.1.0, or from the feed only), so a later rule change can be applied only while the report is still in the outlet's feed.

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