The research team, led by Grace Taiwo Otitoju and colleagues at institutions including Texas Tech University Health Sciences Center El Paso, recruited participants through purposive, community-based sampling. Flyers with QR codes linking to an enrollment form were distributed across the University of Texas at El Paso campus and at community sites including food pantries, health fairs, and libraries. Of 55 people who registered online, 27 attended a session between July and September 2025. Eight sessions were held over Zoom, seven in English and one in Spanish, each lasting roughly 60 to 90 minutes. Sessions with three or more participants were treated as focus group discussions, while two-person sessions were analyzed as dyadic discussions within the same dataset. Participants kept their cameras off for anonymity and were identified only by pre-assigned numbers, with a virtual raise-hand feature used to manage turn-taking.

The demographic profile of the sample reflects the community it represents. Participants had a mean age of 22.5 years, and nearly 60 percent were female. A striking 81.5 percent identified as Hispanic or Latino, 80 percent were born in the United States, and participants had lived in the country for an average of nearly 20 years. Household incomes clustered in the $20,001 to $50,000 range for just over half the group, and nearly half had completed some college or a vocational degree. Self-rated health was generally positive, with more than 80 percent describing their health as good, very good, or excellent. The researchers transcribed each session, verified the transcripts against recordings, and analyzed them thematically, beginning with open coding and a codebook informed by the health belief model’s six constructs: perceived susceptibility, severity, benefits, barriers, cues to action, and self-efficacy. Inductive coding allowed themes outside the model to surface.

The first major theme concerned general awareness and perception of vaccines, and here the picture was broadly favorable. Many participants framed vaccines as essential preventive tools that build immunity against serious illness, with some noting that rising disease levels made vaccination feel newly urgent. Yet perceptions were not formed in a vacuum. Participants repeatedly described vaccination as a social expectation embedded in childhood memory, school enrollment requirements, and family norms. One participant recalled that everyone had to be vaccinated to enroll in school, while another said her family always made sure the children received vaccines for everything. Institutional requirements, from high school health programs mandating tuberculosis and HPV shots to employers requiring full vaccination, functioned as powerful external drivers of uptake. At the same time, cultural conservatism within some families shaped attitudes toward specific vaccines, with one participant observing that some people believe the HPV vaccine encourages sexual activity, a concern documented in prior research on Latino communities.

The second theme mapped directly onto the health belief model’s barrier construct: cost, access, and scheduling. Participants described financial cost as a significant obstacle, and clinics that were far out of the way led some to cancel appointments altogether. Long work hours and competing responsibilities further limited attendance at both vaccination appointments and educational sessions. These structural barriers interacted with psychological ones. Concerns about side effects, particularly of COVID-19 vaccines, were prominent, with participants citing worries about long-term effects and the relative newness of the vaccines. Personal anecdotes carried considerable weight: one participant described a mother who became seriously ill after receiving the COVID-19 vaccine, an experience that fostered a cautious attitude. Such stories illustrate how individual risk perception, a core health belief model construct, is often shaped not by clinical data but by lived experience within families.

Motivations for vaccination, by contrast, were strongly communal. Participants emphasized protecting themselves and others, with several describing vaccination as a way to safeguard immunocompromised loved ones and the broader community. One participant stated a belief in protecting the community by protecting oneself, a framing that extends beyond individual benefit into social responsibility. Within the health belief model, these motivations align with perceived benefits, while school and workplace mandates operate as cues to action. But the researchers argue that community-oriented motivation suggests vaccine communication in this setting should attend to collective responsibility, not merely personal risk calculus. This finding is particularly relevant for a border community where family networks are dense and intergenerational influence is strong, and it hints at messaging strategies that emphasize protecting others rather than only oneself.

Social media emerged as a double-edged sword, and perhaps the most consequential theme for future intervention design. Participants reported frequent encounters with vaccine misinformation and disinformation online, including false claims that vaccines cause autism or kill people, and they expressed frustration that celebrities with large platforms spread such content. Yet some participants described active verification habits, checking claims with their doctors or refusing to take online posts at face value, while acknowledging that many people accept what they see without scrutiny. Notably, the same platforms that carry misinformation, Instagram and TikTok, were identified as the preferred channels for reaching young adults, with engaging formats like memes and funny videos seen as attention-grabbing. The researchers are careful to distinguish preference from credibility: reach does not guarantee trust, and effective peer advocacy should not simply reproduce vaccine messages on popular platforms but must anchor them to credible information from healthcare professionals.

Peer influence itself proved mixed but meaningful. Participants described peer experiences as often the first point of contact for vaccine information, and said that a friend’s good experience with vaccination would encourage them to get vaccinated. Peers were seen as most persuasive when they were relatable or medically knowledgeable. This finding underpins the study’s central practical goal: informing a peer advocacy education curriculum that prepares teens and young adults to promote vaccine confidence in their own networks. Encouragingly, participants expressed substantial willingness to participate in peer-led advocacy, particularly when initiatives were accessible, incentivized, and supported by relatable peers and health professionals. They favored interactive formats such as campus events with food, free vaccine tables at health fairs, and one-on-one conversations that allow people to ask questions without embarrassment. One participant captured the underlying logic succinctly: people are more inclined to listen to those they have things in common with.

Interest in advocacy training was similarly strong, with clear preferences for format. Participants wanted short, hands-on, visually engaging sessions, suggesting time limits ranging from 30 minutes to an hour, and requesting practical tools such as charts of vaccine schedules by age, interactive activities, humor, and the presence of a health professional to answer questions. Scheduling flexibility was flagged as essential, since long work hours were a barrier to attendance, and participants recommended multiple sessions to accommodate different routines. These preferences will shape a proposed curriculum emphasizing 30-to-45-minute modules, visual and role-play activities, bilingual delivery in English and Spanish, digital information literacy training, strategies for responding to common vaccine myths, and culturally sensitive communication with family and peers. The researchers stress that these components remain proposals that must be evaluated in subsequent implementation research before any conclusions about effectiveness can be drawn.

The study’s limitations are acknowledged candidly. The sample was small and drawn from a single county, some sessions included only two participants because registered individuals failed to attend, and recruitment through QR-code flyers at university and community settings may have attracted a self-selected group already inclined toward health engagement. Most sessions were conducted in English despite the bilingual context of the border region. The researchers describe their findings as evidence of thematic adequacy rather than claiming full saturation within each focus group, and they caution that expressed willingness to advocate does not prove that young adults across the broader community would sustain participation. Still, the formative value is considerable. The study demonstrates that the health belief model remains a useful lens for interpreting perceived benefits, barriers, risk appraisal, and cues to action, but that family expectations, peer dynamics, and social media ecosystems must be built into any intervention from the start. In a community where vaccination generates billions of dollars in net economic benefit nationally yet hesitancy persists, equipping trusted young voices with accurate information, professional backup, and practical communication skills may be one of the most promising paths forward.

Subject of Research: Vaccine perceptions, barriers, and peer advocacy among young adults in an underserved US-Mexico border community

Article Title: Applying the health belief model to understand vaccine perceptions, barriers, and motivators and peer influence in an underserved community: A qualitative study

Article References: Otitoju, G. T., Hernandez, A., Sudanagunta, S., Sanchez, K., Dadha, P., & Molokwu, J. (2026). Applying the health belief model to understand vaccine perceptions, barriers, and motivators and peer influence in an underserved community: A qualitative study. Public Health in Practice, 12, Article 100864. https://doi.org/10.1016/j.puhip.2026.100864

Image Credits: AI Generated

DOI: 10.1016/j.puhip.2026.100864

Keywords: vaccine hesitancy, health belief model, peer advocacy, El Paso, young adults, qualitative research, Hispanic community, social media misinformation, HPV vaccine, COVID-19 vaccines, public health, US-Mexico border

The study emerges from the pressing need to address the sheer volume of false information circulating on social media, which traditional human-led fact-checking organizations struggle to counteract effectively due to scalability constraints. The researchers engineered a nuanced experimental setup involving a custom application called FactDeck, designed to simulate social media environments where users encounter headlines of varying credibility. In the controlled setting, 291 participants from across the United States were presented with news headlines either verified by human fact-checkers or flagged by AI systems.

Participants were exposed to different styles of explanatory feedback accompanying fact-checking decisions. One mode, termed “evidence-based,” involved explicitly referencing contradicting information that underpinned the false designation of a post. Another, the “feature-based” explanation, pinpointed suspicious linguistic markers such as irregular phrasing or emotionally charged wording. Lastly, a “black box” approach presented fact-checking results without any explanatory context, reflecting an opaque AI decision-making process.

Intriguingly, the findings underscore what the researchers describe as a “trade-off” in user perceptions. Many respondents attributed AI systems with superior proficiency in scanning and flagging linguistic cues indicative of misinformation, appreciating their ability to methodically evaluate large volumes of data rapidly. However, these same users expressed reservations about AI’s capacity for nuanced judgment and the holistic synthesis of evidence that often characterizes human fact-checkers. Conversely, human verifiers were credited with better interpretive skills and the capacity to corroborate information across multiple, disparate sources—a proficiency currently difficult to replicate by AI.

The concept of “machine heuristics” emerged as a pivotal lens through which users assessed AI fact-checkers. By nature, these heuristics reflect mental shortcuts or stereotypes about machine capabilities and biases. Although participants generally regarded AI as objective and consistent, there was an acknowledged skepticism about AI’s lack of critical reasoning, empathy, and contextual understanding—qualities intrinsic to human cognition. This duality in perception culminated in an equilibrium of trust, where neither AI nor human fact-checking systems dominated user preference.

A particularly salient aspect of the study is the importance of transparency in fact-checking explanations. The researchers noted a clear user preference for explanations, regardless of the specific type, over the absence of any rationale behind a false claim designation. Such transparency enhances user engagement and empowers them to critically appraise the fact-checking process itself, thereby fostering calibrated trust rather than blind acceptance.

Mengqi Liao, the study’s lead author and assistant professor at the University of Georgia, emphasized that this balanced view helps reconcile conflicting results from previous research comparing AI and human trustworthiness. By positing a competing-hypothesis framework, the team highlighted how positive and negative impressions of both fact-checking modalities coexist, collectively neutralizing perceived superiority.

From a technical standpoint, the implications of this research extend beyond mere public perception to the design of future fact-checking systems. Liao advocates for tools that not only deliver precise and reliable verification but also elucidate their decision-making processes. Educating users on the specific strengths and limitations of AI in relation to human judgment may counteract outdated or naïve conceptions of machine intelligence, fostering a more informed and critical citizenry.

S. Shyam Sundar, Evan Pugh University Professor and a key figure in the study, points to the urgency of advancing AI fact-checking capabilities for practical reasons. The velocity and volume of information today far exceed what human fact-checkers can realistically manage. According to Sundar, the ideal model would feature robust human-AI collaboration, wherein human expertise complements AI efficiency. However, in many instances, full automation will become necessary, underscoring the importance of continually refining AI’s capacity to parse and corroborate multifaceted evidence from diverse sources.

The study further acknowledges the role of AI advancements in natural language processing and machine learning, which have ushered in capabilities for detecting subtle linguistic patterns and statistical anomalies that are often hallmarks of misinformation. Recent progress in generative AI models enhances automated fact-checking tools, potentially enabling them to not only detect falsehoods but also generate explainable rationales mimicking human reasoning, albeit at scale.

Nonetheless, the researchers caution against over-reliance on AI alone, given current limitations in contextual understanding and interpretive reasoning. They advocate for an informed deployment of these technologies—one that leverages user education on AI’s functional scope and integrates seamless human oversight when feasible.

In summation, this study highlights the evolving interplay between AI and human roles in the critical task of misinformation detection. The nuanced findings challenge simplistic dichotomies that place humans and machines in rigid competition, instead advocating for a complementary approach informed by empirical insight into user trust and system transparency. As the information ecosystem continues to transform, such research lays a vital foundation for developing effective, trustworthy fact-checking platforms essential in safeguarding democratic discourse and public knowledge.

Subject of Research: Trust dynamics in AI-powered versus human fact-checking systems in the context of misinformation detection.

Article Title: When an AI Says It Is False: User Responses to Misinformation Flagging by Automated vs. Human Fact-Checkers

News Publication Date: 11-May-2026

Web References:
https://www.tandfonline.com/doi/full/10.1080/15213269.2026.2659876

Keywords

Artificial intelligence, Generative AI, Fact-checking, Misinformation, Media Psychology, Machine heuristics, User trust, Social media, Automated verification, Human-machine collaboration

Employing computational simulations, the research team constructed a model of digital agents engaged in information sharing. These agents operated within a binary state environment where the truth could be represented simply as one of two states — akin to conditions such as ‘it is raining’ or ‘it is not raining.’ Each agent held initial beliefs determined by partial observations, reflecting a probabilistic leaning towards one of the states. Critically, agents were designed to be homophilous, demonstrating a higher propensity to interact with other agents whose beliefs closely aligned with their own.

When agents interacted, they exchanged all available observations honestly and then updated their beliefs based on this aggregate information. One might intuitively expect that more information exchange would lead to faster convergence on the true state. However, the simulations revealed a counterintuitive dynamic. Because agents preferentially paired with like-minded individuals, they primarily reinforced their own prior beliefs through reciprocal information sharing. This self-reinforcing loop led to what is known in social theory as polarization, where erroneous beliefs become more entrenched within groups, driving their collective understanding further from the truth.

The implications of this phenomenon are profound for digital communication platforms that champion unregulated information sharing. In real-world contexts, users tend to cluster into echo chambers or filter bubbles, environments where homophily naturally emerges due to shared interests or worldviews. The study’s model suggests that unrestricted information flow in such settings may not promote consensus or truth but instead exacerbate misinformation and ideological divides.

Significantly, the simulation assumes agents with perfect honesty and flawless Bayesian updating, where they update beliefs optimally based on new evidence. Despite this idealized rationality, collective belief accuracy still eroded under conditions of unrestricted information sharing. This finding hints at a potentially even greater vulnerability in real human populations, where cognitive biases, misinformation, and deceptive behaviors further compound these dynamics.

Professor Grossi highlights that introducing constraints on the quantity of information exchanged—limiting the number of observations agents share—can mitigate this accuracy erosion. By restricting information flow, groups are less likely to be locked into self-confirming cycles. This suggests that platform design choices that encourage diverse interactions and moderate information saturation might foster more accurate collective understanding.

This research provides an important lens through which to evaluate democratic principles in the digital age. For digital public spheres to function effectively, platforms must strike a nuanced balance between openness and moderation to prevent destructive polarization. Taking scientific insights seriously can guide the development of digital tools that support healthier public discourse.

The study underscores the need for rigorous interdisciplinary research combining social science, artificial intelligence, and computational modeling to unravel how digital communication reshapes collective cognition. Understanding the interplay between social network structures, information dynamics, and human psychology will be critical in designing online environments conducive to democratic engagement.

As digital platforms increasingly drive how societies form beliefs and make decisions, insights from such simulation studies offer a cautionary note. The simplistic valorization of maximum information dissemination overlooks complex social dynamics that may undermine shared truth. Thoughtful intervention and informed design may be necessary to safeguard collective reasoning in the digital age.

The paper “Free information disrupts even Bayesian crowds,” published in the Proceedings of the National Academy of Sciences, epitomizes the merging of computational theory with social inquiry. By demonstrating how the free flow of information, a cornerstone of social media ideology, can backfire in homogeneous groups, the research challenges technologists and policymakers alike to rethink assumptions about digital communication ecosystems.

Ultimately, this research invites us to reconsider how we construct and regulate the informational environments in which millions now operate daily. Rather than promoting indiscriminate sharing, future social platforms may need architectures that foster heterophily, limit information overload, and prioritize quality and diversity of exposure. Such designs could help avert polarization and misinformation spirals, promoting collective beliefs that better approximate reality.

Subject of Research: Not applicable
Article Title: Free information disrupts even Bayesian crowds
News Publication Date: 1-Apr-2026
Web References: http://dx.doi.org/10.1073/pnas.2518472123
References: Jonas Steina, Shannon Cruz, Davide Grossic, and Martina Testori: Free information disrupts even Bayesian crowds. Proceedings of the National Academy of Sciences, 1 April 2026.
Image Credits: D. Grossi, PNAS
Keywords: Social media, Computer modeling, Information science, Social network theory, Social networks, Homophily