Superlative stacking
- Id
- superlative-stacking
- Status
- Contested
- Severity
- medium
- Detection
- statistical
- Evidence grade
- peer-reviewed
- Languages
- en
- Added
- 2026-08-15
- Updated
- 2026-08-15
Credible people dispute that this signals anything at all.
What it is
Superlatives and absolute intensifiers piled inside a short passage. Ott, Choi, Cardie and Hancock measured more superlatives in deceptive hotel reviews than in truthful ones in 2011, more than a decade before any of this. That finding is about human deception. It carries no claim about who or what wrote a text, and it is included here as a low-effort signal on exactly those terms.
Why it reads as machine-made
Praise with no interior is cheap to produce at any length. The same 2011 paper frames deceptive writing as imaginative rather than informative and reports that people inventing an experience have trouble encoding concrete spatial detail. That is the useful reading of a stack: look at what the passage fails to contain rather than at the adjectives it contains. The same authors found human judges performing at roughly chance on the task, which is the strongest reason to treat any stack as a prompt to read rather than a conclusion.
Specimens
Absolutely perfect from start to finish. Hands down the best experience we have ever had, and the staff were incredibly amazing throughout.
We arrived at 23:00 and the kitchen reopened to make one plate of pasta. The room was above the bins, which we would mention to anyone booking in July.
How it is detected
- Metric
- superlative-and-absolute-intensifier-density-per-1000-words
- Threshold
- 8
- Direction
- above
- Threshold basis
- Ott and colleagues report that deceptive reviews contain more superlatives than truthful ones and publish no per-thousand-word rate for either class. No other source publishes one. Eight per 1,000 words is a FeedSquad review trigger set by arithmetic rather than measurement: a 250-word review crosses it on the third stacked superlative. Treat a crossing as a reason to read the passage for concrete detail. It is not a verdict, and it is not evidence about authorship.
Who writes this way legitimately
Enthusiastic real customers write exactly like this, and so do fans, awards copy, restaurant blurbs and anyone describing a holiday they loved. Ott and colleagues measured the pattern as a deception signal in human text in 2011, which is a claim about effort and invention rather than about tooling, and the same paper reports human judges at roughly chance when they try to call individual reviews. A reviewer should look for one physical specific instead: a floor number, a dish, a time, a name, a street. A passage that stacks superlatives and also contains such details is a happy customer. A passage with nine superlatives and nothing a person could have seen is worth reading twice.
Model attribution
Not a model signal. The peer-reviewed source predates chat products by over a decade and studies human writers. vale-ai-tells lists the phrases as assistant output as well, with no measurement, so the honest reading is that the pattern marks low-effort praise regardless of who produced it.
Platform notes
- amazon
- Fake-review detection there is described as behavioural and graph-based, with no stylistic criterion published. Style is the amateur method on this surface; volume, coordination and payment signals are what the platform describes using.
Status history
| Date | Status | Rationale |
|---|---|---|
| 2026-08-15 | Contested | Opened as contested. The peer-reviewed evidence is a 2011 study of human deceptive reviews, which is a different claim from a machine signal, and the same paper found human judges at roughly chance on the task. The entry ships with that boundary stated and with a threshold declared as a review trigger. |
Sources
- 01Ott et al., Finding Deceptive Opinion Spam by Any Stretch of the Imagination, ACL 2011peer-reviewedaccessed 2026-08-14
- 02vale-ai-tells, 111 machine-checkable rulescommunityaccessed 2026-08-14
CC BY 4.0 / The AI Tells Index, feedsquad.com/ai-tells