KFF Poll Shows Public Uncertainty Regarding Common Vaccine Myths

KFF Polling Data Reveals Public Uncertainty Regarding Vaccine Myths

The “Mixed Middle” and Vaccine Misinformation

Recent polling from KFF indicates that belief in vaccine misinformation is not as entrenched as some might assume. The analysis examines the pervasiveness of several common vaccine myths, including that MMR vaccines have been proven to cause autism; that measles vaccines are more dangerous than measles; that more people died from COVID-19 vaccines than from the virus; and that mRNA vaccines can change your DNA. Over the past several years, fewer than one in ten adults have said each of these myths are “definitely true,” while much larger shares—though fewer than half of adults—say they are “definitely false.”

At least half of the public consistently falls into the “malleable middle,” expressing some uncertainty and saying the myths are either “probably true” or “probably false.” This dynamic has remained relatively stable over several years of KFF polling, though there have been some minor shifts in the share who say some of these myths are “definitely false,” reflecting how beliefs are not often completely fixed. In a “Beyond the Data” column, KFF’s Founding President and CEO Drew Altman writes that Americans who firmly believe vaccine myths are vastly outnumbered both by those who believe science and those who remain uncertain. He suggests that amplifiers, including influencers, officials, and the media, may spread confusion and uncertainty about health claims.

Researchers Deploy Machine Learning to Identify Falsehoods

AI as a Tool for Verification

While artificial intelligence is frequently cited for its role in spreading misinformation—such as the 2024 robocalls in New Hampshire featuring an AI-synthesized voice of Joe Biden—researchers are now actively exploring ways to use the same technology to combat it. Scientists are finding that AI’s ability to parse human language, summarize text, and verify claims could be harnessed to help people identify fake news. Machine learning, a type of AI where computers learn patterns in data to make predictions, has long been used to identify falsehoods by analyzing textual features like the overuse of capital letters, exclamation points, or emotionally charged language.

Experts stress that these methods should never be used without some level of human supervision. However, researchers see AI as an important ally against misleading information. As Jevin West, an expert on misinformation and generative AI at the University of Washington, states: We should fight fire with fire.

Pew Research Center Survey Highlights Global Concerns Over Online Misinformation

Challenges and Future Directions

The necessity for such tools is underscored by a recent survey from the Pew Research Center, in which most adults across a diverse range of nations identified online misinformation as a major threat to their country. While researchers hope to combat this in a large-scale, systematic way, they acknowledge that AI does not have a great reputation for veracity. AI has been utilized to create fake social media accounts, generate clickbait, and produce content for farms that exist solely to collect ad revenue.

Health Communicators Target Malleable Middle Groups Based on KFF Findings

The KFF findings suggest that public health communication strategies must account for the nuances of the “mixed middle.” By identifying patterns of belief across these myths, health communicators hope to address the specific uncertainties expressed by these groups.

Find more reporting in our Health section.