Research shows that being called out by peers, not algorithms or experts, makes online authors think twice about spreading misinformation.
When the social media platform invited users to flag false or misleading posts, critics initially scoffed. How could the same public that spreads misinformation be trusted to correct it? But a by researchers from the 暗网吃瓜, the University of Illinois Urbana鈥揅hampaign, and the University of Virginia finds that 鈥渃rowdchecking鈥� (X鈥檚 collaborative fact-checking experiment known as Community Notes) actually works.
The paper, published in the journal Information Systems Research, shows that when a community note about a post鈥檚 potential inaccuracy appears beneath a tweet, its author is far more likely to retract that tweet.
鈥淭rying to define objectively what is misinformation and then removing that content is controversial and may even backfire,鈥� notes coauthor , the Xerox Professor of Information Systems and Technology at 暗网吃瓜鈥檚 . 鈥淚n the long run, I think a better way for misleading posts to disappear is for the authors themselves to remove those posts.鈥�
Using a causal inference method called regression discontinuity and a vast dataset of X posts (previously known as tweets), the researchers find that public, peer-generated corrections can do something experts and algorithms have struggled to achieve. Showing some notes or corrective content alongside potentially misleading information, Rui says, can indeed 鈥渘udge the author to remove that content.鈥�
Community Notes on X: An experiment in public correction
Community Notes operates on a threshold mechanism. For a corrective note to appear publicly, it must earn a 鈥渉elpfulness鈥� score of at least 0.4. (A proposed note is first shown to contributors for evaluation. The bridging algorithm used by Community Notes prioritizes ratings from a diverse range of users鈥攕pecifically, from people who have disagreed in their past ratings鈥攖o prevent partisan group voting that could otherwise manipulating a note鈥檚 visibility.) Conversely, notes that fall just below that threshold stay hidden to the public. That design allows for a natural experiment as the researchers were able to compare X posts with notes just above and below the cutoff (i.e., visible to the public versus visible only to Community Notes contributors )鈥攖hereby enabling them to measure the causal effect of public exposure.
In total, the researchers analyzed 264,600 posts on X that received at least one community note during two separate time intervals鈥攖he first before a US presidential election, which is a time when misinformation typically surges (June鈥揂ugust 2024), and the second two months after the election (January鈥揊ebruary 2025).
The results were striking: X posts with public correction notes were 32 percent more likely to be deleted by the authors than those with just private notes, demonstrating the power of voluntary retraction as an alternative to forcible removal of content. The effect persisted across both study periods.
The reputation effect
An author鈥檚 decision to retract or delete, the team discovered, is primarily driven by social concerns. 鈥淵ou worry,鈥� says Rui, 鈥渢hat it鈥檚 going to hurt your online reputation if others find your information misleading.鈥�
Publicly displayed Community Notes (highlighting factual inaccuracies) function as a signal to the online audience that 鈥渢he content鈥攁nd, by extension, its author鈥攊s untrustworthy,鈥� the researchers note.
In the social media ecosystem, reputation is important鈥攅specially for users with influence鈥攁nd speed matters greatly, as misinformation tends to spread faster and farther than corrections.
The researchers found that public notes not only increased the likelihood of tweet deletions but also accelerated the process: among retracted X posts, the faster notes are publicly displayed, the sooner the noted posts are retracted.
Those whose posts attract substantial visibility and engagement or who have large follower bases, face heightened reputational risks. As a result, verified X users (those marked by a blue check mark) were particularly quick to delete their posts when they garnered public Community Notes, exhibiting a greater concern for maintaining their credibility.
The overall pattern suggests that social media鈥檚 own dynamics, such as status, visibility, and peer feedback, can improve online accuracy.
A democratic defense against misinformation?
Crowdchecking, the team concludes, 鈥渟trikes a balance between protecting First Amendment rights and the urgent need to curb misinformation.鈥� It relies not on censorship but on collective judgment and public correction. The algorithm employed by Community Notes emphasizes diversity and views that are supported by both sides.
Initially, Rui admits, he was surprised by the team鈥檚 strong findings.
鈥淔or people to be willing to retract, it鈥檚 like admitting their mistakes or wrongdoing, which is difficult for anyone, especially in today鈥檚 super polarized environment with all its echo chambers,鈥� he says.
At the outset of the study, the team had wondered if the correcting mechanisms might even backfire. In other words, could a public display note really induce people to retract their problematic posts or would it make them dig in their heels?
Now they know it works.
鈥淯ltimately,鈥� Rui says, 鈥渢he voluntary removal of misleading or false information is a more civic and possibly more sustainable way to resolve problems.鈥�
