The correction rarely gets the same opening weekend as the mistake.
A false claim can arrive first, spread widely, accumulate screenshots, become a joke, enter arguments, and get repeated by people who never saw its original source.
Then the correction appears.
Different day. Smaller audience. Worse headline.
That does not mean corrections are useless.
It means the comparison is structurally unfair.
Early impressions can persist
Psychologists use the term continued influence effect for the finding that misinformation can continue affecting reasoning even after people have received a correction.
A meta-analysis by Nathan Walter and Riva Tukachinsky covering 32 studies found that corrections reduced misinformation’s influence but did not always eliminate it completely. See the published abstract and study.
Later reviews reach a similarly important but less dramatic conclusion: factual corrections generally improve belief accuracy, and evidence for a broad “backfire effect”—where corrections routinely make people more wrong—is weak. See the 2024 review Factual corrections: Concerns and current evidence.
So the useful statement is not corrections fail.
It is corrections do not automatically reset the information environment to zero.
Audience overlap is a separate problem
Suppose a misleading post reaches two million people.
A careful correction reaches 80,000.
The correction could be extremely persuasive among the people who see it and still leave most of the original audience untouched.
The 2024 review notes that people exposed to misinformation may be unlikely to encounter the relevant correction at all.
That turns distribution into part of the correction problem.
A false consensus can therefore persist not only because people reject corrections, but because many people never receive them.
Corrections need their own measurement
Researchers should separate at least three questions:
Did the correction reach the original audience?
Did recipients update their factual belief?
Did that change affect later attitudes or behavior?
Those are not the same outcome.
A platform adding a label may increase factual accuracy without changing a person’s opinion of the larger issue. A corrected statistic may disappear from later discussion while the emotional impression it helped create remains.
Conversely, a strong correction can work very well when it reaches people with clear evidence and an alternative explanation.
False consensus has a memory
Imagine a company is falsely accused of receiving thousands of identical complaints.
The fabricated screenshots spread for three days.
The platform later proves the accounts were coordinated and removes them.
Anyone who sees that evidence can revise their view.
But copies may remain elsewhere, search results may retain old headlines, and people may continue saying, “Didn’t everybody hate that company last year?”
The correction has to chase an impression that already escaped its original container.
Manufactured Consensus is therefore not only about creating a crowd.
It is about the residue that crowd can leave behind.
Corrections matter.
They just have to run uphill after the first story already learned the route.