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

The study, presented under the title Tracking Pediatric Health Misinformation Clinicians Hear to Build an Institutional Response, describes a practical surveillance system built inside a large pediatric hospital. Rather than treating each misinformation encounter as an isolated event to be corrected on the spot, the CHOP team created a structured way to log those encounters, aggregate them, and analyze the patterns that emerged. The result is a feedback loop in which the questions and doubts voiced in exam rooms directly shape the educational materials the institution produces for patients, families, and its own clinicians.

The technical backbone of the system is a survey tracking tool built on REDCap, a widely used electronic data capture platform in academic medical research. The survey was distributed across CHOP clinical environments through fliers, emails, and a link embedded directly in the hospital’s electronic health record system, Epic, making it easy for busy clinicians to report misinformation they encountered during routine care. Respondents were asked what misinformation they had heard, which category it fit into, whether they felt prepared to address it, what strategies they used, and whether or not their approach worked, along with additional demographic questions about the respondent.

To make the collected data actionable, the researchers paired the survey with a Qliksense dashboard that provides real-time insights into survey entries. A multidisciplinary team, including a pediatrician, a neonatologist, public relations and marketing specialists, a data analyst, and a journalist, meets monthly to review the trending topics and decide which ones warrant new patient education content or internal clinician training. This combination of lightweight data collection, live analytics, and regular cross-disciplinary review is what transforms scattered anecdotes into an institutional response capable of keeping pace with rapidly shifting misinformation trends.

Between May 2025 and March 2026, the tracking tool recorded 234 entries from clinicians across the hospital. The single largest category was vaccine misinformation, which accounted for 53.8 percent of all entries, confirming that immunization remains the dominant battleground for pediatric health myths. Trending keywords included aluminum, acetaminophen, ADHD, and antibiotics, reflecting the specific concerns circulating in the community the hospital serves. Beyond vaccines, the topics ranged across categories such as distrust in healthcare, supplements, miracle cures, and unnecessary interventions, illustrating how broad and varied the misinformation landscape confronting pediatric clinicians has become.

The survey also yielded revealing data about how prepared clinicians feel when confronting misinformation, and how effective they actually are. On a scale of 1 to 100, respondents reported a median preparedness score of 77 in responding to misinformation from patients. Yet only 28 percent reported that their approach actually worked in changing a patient’s or family’s perspective. That gap between confidence and effectiveness is one of the study’s most striking findings, and it underscores a problem familiar to communication researchers: simply knowing the science is often not enough to counter a belief that is emotionally or socially anchored.

Experience, interestingly, cut in both directions. Median preparedness scores increased with years of practice, suggesting that seasoned clinicians feel more comfortable engaging with misinformation. However, more years in practice was associated with lower chances that their approach actually worked. The findings hint that the communication techniques that come naturally to experienced physicians may not be the ones best suited to changing minds, and that formal training in misinformation response could benefit clinicians at every career stage. It was precisely this kind of insight that informed the internal education initiatives the hospital subsequently developed.

The practical output of the tracking system has been substantial. The entries have driven the creation of new patient education and public health social media content on 28 topics, including videos and blog posts, along with seven corresponding opinion editorials published in the Philadelphia Inquirer. The external-facing component is a website called Pediatric Health Chat, which contains educational information specifically designed to debunk the misinformation families reported encountering in their everyday lives. In addition, the team launched a weekly educational email on vaccine communication skills, which has run for ten weeks and counting of clinician education content informed directly by the REDCap entries.

Katie Lockwood, MD, MEd, FAAP, study author and pediatrician at CHOP, framed the approach as a way of listening to families more systematically. Looking at these trends allowed the team to see in real time what was worrying the families they care for and to give them the most up-to-date science on topics of interest, she said. Addressing pediatric health misinformation is vital to protecting the health of children, she added, and as trusted messengers, children’s hospitals have an obligation to respond. The research showed that systematically tracking the misinformation families encounter can turn clinicians’ real-world questions into effective, targeted education, even when individual corrective conversations fall short.

The implications extend well beyond a single institution. Although fewer than one-third of survey respondents said their own responses worked, the aggregated findings gave providers a clearer understanding of families’ concerns and helped guide materials for patients, families, and other clinicians. The researchers state that the approach could be replicated in other hospitals and care settings to help build trust with patients and caregivers and provide them the information they need most to keep children healthy. In an era when misinformation spreads faster than any press release, the CHOP model offers a template for meeting families where their worries actually are, using the clinical encounter itself as a sensor for the information environment surrounding children’s health.

Subject of Research: Tracking pediatric health misinformation in clinical encounters to build institutional patient education responses

Article Title: Monitoring online health misinformation can help create useful patient education content

Article References: Monitoring online health misinformation can help create useful patient education content. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: health misinformation, pediatrics, vaccine misinformation, patient education, Children's Hospital of Philadelphia, American Academy of Pediatrics, clinical communication, REDCap, clinician training, public health messaging, social media, Pediatric Health Chat