This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Election rumors, such as unverified accounts of voter fraud, have run rampant in the U.S. in recent years. Powered by the speed of social media and artificial intelligence (AI), it can be hard for people to know what messages to trust.
Now, a team led by Caltech researchers is using AI as a tool for good in the fight against election misinformation. In a recent paper published in the journal Royal Society Open Science, the team describes an AI tool it built to develop "pre-bunking" messages, a proactive communication technique intended to prevent the spread of misinformation by warning people of false claims before they encounter them. Testing of the scalable AI-assisted framework, which generates pre-bunking articles, showed the system mitigated a decrease in election confidence due to rumors among sampled voters.
This effect was still measurable a week later and held across party lines. "False claims can spread widely before fact-checkers have time to respond," says Mitchell Linegar (PhD '26), lead author of the paper who is now a postdoctoral scholar at Washington University in St. "Pre-bunking gives people accurate information before exposure, making them less likely to believe these claims, potentially stopping their spread before it starts." Linegar worked on methods to systematically counter political misinformation during his doctoral studies with R.
Michael Alvarez, Caltech's Flintridge Foundation Professor of Political and Computational Social Science and co-director of the Caltech/MIT Voting Technology Project. Together with Betsy Sinclair (PhD '07), chair of political science at Washington University and a research affiliate with Caltech's Linde Center for Science, Society, and Policy (LCSSP), and Sander van der Linden, professor of social psychology at the University of Cambridge, Linegar and Alvarez sought to use large language model–generated information to help voters navigate a complicated media environment where it is tough to separate truth from fiction. "The rise of AI has meant that fiction is now easier and cheaper to produce—while verified truthful information is still expensive to produce—so voters are facing an onslaught of election rumors, misinformation and disinformation," Sinclair explains.
"Pre-bunking with AI is one strategy we can use to fight back and to empower voters to sort out what is true." There has been scientific consensus for many years that pre-bunking is effective, she says, but producing it at scale has been difficult because it has required a lot of human effort to produce each pre-bunking intervention. But by combining a reusable human expert–crafted prompt with verified election information, the model developed by the team could generate pre-bunking articles for new rumors quickly and without the need for further human review. The researchers tested the model using five common myths about the 2024 U.S. election prior to Election Day in a sample of more than 4,000 registered voters.
Participants were randomly assigned to read a persuasive human-written article endorsing one of five commonly believed election myths. Some then received an AI-generated pre-bunking article addressing the myth, while others received an AI-generated article about an unrelated subject. After the intervention, the researchers measured the participants' beliefs in election myths, confidence in true election facts and overall trust in election integrity, with follow-up measurements one week later.
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