Right Tone, Not Right Source: How AI Wins at Correcting Health Misinformation

Artificial intelligence is increasingly being deployed on social media platforms to counter the relentless spread of false health claims, but a new study from Washington State University suggests that the machinery of persuasion is more subtle than simply having an algorithm deliver correct facts. According to research led by the university and published in the International Journal of Human-Computer Interaction, AI-generated corrections of medical misinformation can be genuinely effective, yet their success hinges less on who or what delivers the correction and more on how the message is framed. The tone of a correction, the researchers found, matters more than whether it comes from a human being or an AI agent, and the winning tone depends on the beliefs of the person being corrected.

The study was led by Porismita Borah, a professor in Washington State University’s Edward R. Murrow College of Communications and corresponding author of the new publication. Her co-authors were Ziyao Zhang, a PhD student at WSU; Xiaohui Cao, a PhD student at the University of Wisconsin-Madison; and Danielle Ka Lai Lee, an assistant professor at Hong Kong Shue Yan University. Their work addresses one of the most stubborn problems in the misinformation literature: corrections often fail, and even when they succeed, it has been unclear why. People who are told that a claim they believe or shared is wrong can feel challenged or insulted, react defensively, or simply doubt the credibility of whoever is doing the correcting.

Borah has spent roughly a decade studying how misinformation spreads and how it might be countered, and her latest publication advances the field’s understanding of how empathy functions in correction messages. Prior research in this area has produced conflicting conclusions, with some studies finding that a warm, understanding tone reduces misperceptions and others finding no such effect. Borah’s team added a crucial new layer to the question by testing the effectiveness of tone against the expectations and beliefs of the person receiving the correction, rather than treating all audiences as interchangeable.

The central concept in that layer is anthropomorphism, the well-documented psychological tendency to assign human characteristics to animals, machines, and other non-human entities. In the context of conversational AI, anthropomorphism describes the degree to which a user perceives an AI system as humanlike rather than as a purely technical tool. The study’s key insight is that the optimal communication strategy differs across this spectrum. For people who view AI strictly as a machine, corrections delivered in a neutral, just-the-facts tone proved most persuasive. For those who believe AI can be humanlike, an empathetic, understanding tone worked best.

What makes the finding striking is what did not matter. In multiple studies, the team found that corrections could work most of the time, but the source of the correction was largely irrelevant to its persuasive power. As Borah explained, it did not necessarily matter whether the correction came from a human being or an AI agent; what mattered was the tone and how that tone aligned with people’s beliefs about whether AI agents should behave more like humans or more like machines. In other words, a mismatch between message style and user expectations could blunt even an accurate correction delivered by a well-designed system.

To test these effects rigorously, the researchers conducted a randomized online experiment with 857 parents of children within the age range recommended to receive the vaccine for human papillomavirus, or HPV. The choice of topic was deliberate. HPV is spread through sexual contact and can cause a range of serious health problems, including several cancers. The HPV vaccine is considered safe and effective, yet it has been the subject of widespread and persistent misinformation, making it an ideal case for studying how false health beliefs form and how they might be corrected.

Participants were first evaluated for their level of anthropomorphism belief, then shown a simulated Facebook comment thread. The thread began with a false claim stating that HPV vaccines increase the risk of neurological problems. An AI corrections account then engaged with the claim directly in the comment thread, and the style of its response varied. The neutral answers used direct, plain language, along the lines of stating that the claim was not true and that scientific studies had shown no link between HPV vaccines and the neurological conditions in question. The empathetic tone was warmer, opening with an acknowledgment of the person’s concern, such as expressing that the researchers heard the individual, before pivoting to the scientific evidence.

The results were clear: the correction was most effective at reducing misperceptions when its tone matched the respondent’s anthropomorphism beliefs. A machinelike user confronted with a just-the-facts correction updated their beliefs; a user inclined to see AI as humanlike responded better to warmth and understanding. The finding adds an important dimension to the technical design of automated fact-checking systems, because it implies that a one-size-fits-all correction strategy will systematically fail for part of any large audience. Matching conversational style to individual expectations of the technology could unlock measurably better outcomes at population scale.

The practical implications reach well beyond academic journals. The authors suggest that social media platforms, government agencies, news organizations, and other institutions that confront health misinformation could target their AI fact-checking agents according to a user’s level of anthropomorphism. One proposed approach is a simple onboarding step for users of platforms or accounts that assesses their anthropomorphic tendencies, allowing the AI’s conversational tone to be adjusted accordingly. Such an intervention would be technically lightweight compared to the far harder task of changing underlying beliefs, and it could be integrated into existing comment-moderation and community-notes systems that already deploy AI to flag or respond to dubious claims.

As Borah notes, the problem of misinformation is critical and it is not going away, and the effectiveness of corrections depends on many factors, including the way accurate information is communicated; an empathetic tone may often work better than a condescending one, while factors such as race and gender also shape how messages land. Her conclusion is a sober reminder for engineers and communicators alike: researchers are ultimately trying to study humans, and humans are remarkably complex. The lesson of this study is not that AI is a silver bullet against falsehood, but that the oldest rule of persuasion, know your audience, applies with full force even when the messenger is a machine.

Subject of Research: How the tone of AI-generated corrections, matched to users' anthropomorphism beliefs, reduces vaccine misinformation

Article Title: AI can help correct medical misinformation — when it uses the right tone

Article References: AI can help correct medical misinformation — when it uses the right tone. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, misinformation, health communication, HPV vaccine, anthropomorphism, vaccine misinformation, social media, empathy, fact-checking, persuasion, human-computer interaction, Washington State University