The sentiment-divergence test is a proposed method for measuring whether AI systems still reproduce community consensus, by querying categories where community opinion and official framing visibly disagree and observing which one the answer echoes.
The procedure is deliberately simple. Select product or service categories in which the prevailing community verdict differs measurably from the manufacturer and trade-press verdict — cases where enthusiasts consider a well-reviewed product overrated, or an unglamorous one quietly excellent. Put neutral questions to a 📝Large Language Model (LLM) and record which framing the response reproduces, without regard to what it cites or whether it cites anything. Repeat on a fixed cadence so the series can be read as a trend rather than a snapshot.
Its value is that it observes the output instead of the attribution, which is precisely the blind spot described in 📝Citation Share as a Proxy Metric. If a model's answers continue tracking community sentiment while its citations move elsewhere, the culture layer of 📝Learning Source vs. Trust Source vs. Culture Source is intact and the citation decline is a visibility story. If answers migrate toward official framing, that layer is genuinely eroding, and the thesis carried in 📝How Reddit's 'Authenticity Shockwave' Forged Its True Long-Term Value would be failing on its own terms rather than surviving another citation drop.
The test was specified in response to 📝Reddit's ChatGPT Citation Collapse (August 2026), on the reasoning that a second sharp citation decline in under a year is a reason to build a better instrument rather than to re-argue the previous one.
Three secondary indicators point the same direction at lower resolution: the terms on which AI providers renew their content licensing, since a renewal reprices the influence directly; a platform packaging cultural signal as a standalone commercial product, which would suggest its buyers are no longer only model developers; and whether complaints about model output quality resolve without social content returning to the mix. None substitutes for the direct test, and the test itself is specified here but not yet run.
I would rather publish the instrument unrun than a rough twelve-query pass dressed up as evidence. A cluster arguing against repricing on provisional data does not get to make an exception for its own numbers.