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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteMisinformation spreads when attention-grabbing claims are easy to share through human networks. Effective corrections do more than label a claim false: they lead with what is accurate, explain the error with credible evidence, and give readers a coherent account to replace the false one. The evidence supports several useful approaches, but no single correction method works best in every context.
What misinformation means—and what the term does not tell you
Misinformation is false information shared without an intent to mislead; disinformation is false information shared with that intent. That distinction follows the World Health Organization’s 6 February 2024 definition. Falsity alone does not establish that someone knowingly deceived others, so it is better to describe a claim or its accuracy than to infer a sharer’s motive.
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There is also no single authoritative current measure of how much misinformation exists across all online platforms. Findings about one platform, time period or topic should not be treated as a universal rate.
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Novel claims can attract attention
A large Twitter study by Soroush Vosoughi, Deb Roy and Sinan Aral, published in 2018, examined about 126,000 verified true and false stories distributed from 2006 to 2017. Roughly 3 million people tweeted those stories more than 4.5 million times. Within that historical dataset, false stories traveled farther, faster, deeper and more broadly than true stories.
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The false stories in the study were more novel than the true ones. Novelty may help explain why people shared them, but the study’s observational findings do not prove that novelty alone caused sharing. They also do not establish that the same pattern holds across every platform, ranking system, topic or period.
Emotional reactions and social signals may play a part
Replies to false stories in the Twitter study more often expressed fear, disgust and surprise. Replies to true stories more often expressed anticipation, sadness, joy and trust. These are observed associations, not proof that a particular emotion independently makes people share a claim.
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Social proof—using cues about what other people believe or do—is another possible influence. The WHO Regional Office for Europe discusses it as a behavioral concept relevant to prebunking and encouraging accurate beliefs and behaviors. It is one possible mechanism, not a complete explanation of online spread.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsPeople, not bots alone, amplified the false stories in that dataset
The 2018 study found that bots accelerated the spread of true and false stories at similar rates. The authors therefore concluded that humans were more likely to spread the false stories in their dataset. This finding does not mean bots never matter; it means bot activity alone did not explain the difference in diffusion observed in that particular analysis.
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What makes a correction effective?
A useful correction helps someone understand both what is accurate and why the original claim was wrong. A review by Ecker and colleagues in Nature Medicine (2022) describes several elements of sound correction design:
- Lead with the accurate account. Put the fact readers need first instead of opening by repeating the false claim.
- Identify the inaccurate claim only as much as needed. Make clear what is being corrected, then explain the specific error rather than relying on a bare “false” label.
- Show the basis for the correction. Use relevant, credible evidence, such as an authoritative expert source or scientific consensus. Keep the explanation accessible and no more complicated than necessary.
- Offer a coherent alternative explanation. Give readers an accurate account they can use in place of the faulty one.
- Keep the response proportionate. Address the claim without implying that every source or factual statement is untrustworthy.
These are design principles, not a guarantee that every reader will accept a correction. A separate set of experiments found that some interventions reduced belief in false claims while also reducing perceived credibility of factual information. That spillover is a reason to distinguish careful correction from broad, indiscriminate warnings.
Prebunking, debunking and accuracy prompts compared
These approaches target different moments and outcomes. “Works” can mean a change in belief, perceived credibility or intention to share; those measures are not interchangeable.
| Approach | When and what it targets | What the reviewed evidence found |
|---|---|---|
| Prebunking | Before exposure to a false claim; builds recognition of inaccurate information or manipulation tactics. | The WHO Regional Office for Europe’s 2024 toolkit describes it as providing accurate information before false information spreads and equipping people to identify inaccuracies. A preregistered four-country experiment found expected improvements from prebunking. |
| Debunking | After exposure; corrects a specific false claim and explains the accurate account. | In the same four-country experiment, debunking also improved outcomes in the expected direction and was slightly more effective overall in that study. This is not evidence that debunking always outperforms prebunking. |
| Accuracy prompts | At a sharing decision; directs attention to accuracy before someone decides whether to share. | Pennycook and colleagues’ 2022 analysis found that prompts primarily reduced intentions to share false headlines, improving sharing discernment. The reported 10% relative reduction applies to false-headline sharing intentions versus control in the studies analyzed, not to a guaranteed reduction in actual platform sharing. |
The four-country online experiment by Bruns and colleagues (2024) included 5,228 participants in Germany, Greece, Ireland and Poland, who encountered climate-change or COVID-19 misinformation. Disclosing the intervention source did not significantly change effectiveness overall, although the study found a trust-related exception for one outcome. Its results apply to the tested settings and outcomes, not to every audience or topic.
A review by Altay and colleagues in Nature Human Behaviour (2024) synthesized 81 scientific papers and categorized nine types of individual-level interventions. The breadth of approaches helps explain why “Do fact-checks work?” has no context-free yes-or-no answer: the outcome, audience, topic and intervention design matter.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can correcting misinformation backfire?
It can have an unintended spillover effect. Across three online experiments totaling 6,127 participants in the United States, Poland and Hong Kong, Hoes and colleagues (2024) found that prominent misinformation interventions reduced misperceptions but also increased skepticism toward factual information. An author correction was issued on 19 July 2024.
This does not mean corrections should be avoided. It means their scope and tone matter: correct the claim at issue, show the evidence, and avoid turning a specific warning into a suggestion that reliable information in general cannot be trusted. Judge an intervention by both its intended outcome and any loss of confidence in accurate information.
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How to choose an approach for a real claim
- If a claim has not yet circulated widely: consider prebunking—provide accurate information early and help people recognize the kinds of inaccuracies they may encounter.
- If a specific false claim is already circulating: use a debunk that states the accurate account, explains the error and supplies a credible basis and alternative explanation.
- If the immediate concern is whether someone will share a headline: an accuracy prompt may help them pause, but evidence about sharing intentions should not be presented as proof of a change in actual sharing.
- If trust is low or the topic is contested: make the source and evidence legible, and consider whether the intervention could cause readers to discount accurate information as well.
- When assessing results: specify whether the measure is belief in a claim, perceived credibility or sharing intention, and account for the topic, country, platform and experimental setting.
The evidence reviewed here includes observational analysis, experiments, a research review and WHO guidance. Experiments show effects in their tested conditions, not guaranteed population-wide outcomes; the historical Twitter findings cannot settle how current platforms compare. No universal winner between prebunking and debunking is established.
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