The research team began by assembling a candidate pool of quality indicators drawn from existing evaluation instruments and the published literature on health information quality. This starting library reflected the accumulated wisdom of information science and health communication research, covering familiar concepts such as accuracy, authority, and objectivity. But rather than imposing expert judgments from the top down, the researchers then designed a questionnaire survey aimed squarely at mobile social media users themselves. Their goal was to capture user perception as the organizing principle of the evaluation framework, on the reasoning that an assessment scheme which ignores the expectations of its end users risks being technically sound but practically irrelevant.

Methodologically, the study combined two analytical strategies. First, respondents were asked to rate the importance of each candidate indicator, allowing the researchers to rank the indicators according to user suggestions. Second, the team applied hierarchical clustering analysis, a statistical technique that groups items based on the similarity of their response patterns, to identify natural dimensions of indicator classification. Hierarchical clustering builds a tree-like structure in which each indicator starts as its own cluster and neighboring clusters are merged step by step according to a distance measure, so the final grouping emerges from the data rather than from preconceived categories. The researchers then refined these statistical clusters with professional practice, producing a framework that is both empirically grounded and operationally usable.

The result is a five-dimensional organization of health information quality indicators for mobile social media. Four of the dimensions concern the health information content itself: relevance, timeliness, credibility, and professional value. Relevance captures whether the information matches a user’s actual health situation and needs. Timeliness reflects how current the content is, a critical attribute in a field where guidance evolves rapidly. Credibility speaks to the trustworthiness of the claims and their sources, while professional value addresses whether the content carries genuine clinical or scientific substance rather than superficial wellness talk. The clustering analysis, supplemented by expert input, showed that these content-oriented properties form coherent, distinguishable axes along which users implicitly evaluate what they read on their phones.

Beyond the content dimensions, the framework incorporates platform-level and service-level indicators that reflect the distinctive affordances of mobile social media, such as navigation, personalized recommendation, and the presence or absence of advertising. This is precisely where conventional evaluation criteria fall short. Traditional health information quality tools were built for websites, printed materials, or institutional sources, and they have no natural way to account for algorithmic feeds, recommendation engines, or the commercial pressures embedded in platform design. By grounding the indicator set in user perception, the study ensures that these platform-specific factors receive the same analytical attention as the classical content attributes, producing an evaluation model that matches the environment in which people actually encounter health information.

One of the most practically valuable findings of the study concerns demographic consistency. Subgroup analysis revealed no statistically significant differences across demographic groups in the perceived importance of several key indicators, including authentication, objectivity, author transparency, reference source, and readability. In other words, users across different ages, backgrounds, and population segments appear to agree that knowing who authored a post, whether the author is credentialed, what evidence is cited, and how easily the text can be understood are all matters of shared concern. This consensus simplifies the task of platform designers and regulators, because it identifies a common baseline of quality expectations that can be applied universally without tailoring to specific demographic segments.

The same consistency extended to another cluster of indicators. Users from different groups showed aligned recognition of the importance of navigation, personalized recommendation, content relevance, the absence of advertising, content focus, and the universality of health programs. These indicators describe the experience of finding and consuming health content: whether users can move through information efficiently, whether recommendations match their needs, whether the content stays on topic, and whether commercial distractions interfere with the health message. The demographic agreement on these points suggests that platforms can pursue improvements in these areas with confidence that the changes will be valued broadly across their user base, rather than serving only a narrow slice of the audience.

Not every indicator, however, enjoyed universal endorsement. The study found significant differences among user groups in the perceived importance of multimedia richness, the degree to which health information is presented through varied formats such as video, audio, images, and interactive elements. This divergence is instructive. It implies that the optimal presentation format for health content is not a one-size-fits-all decision: some users may rely heavily on visual and video formats to understand medical concepts, while others may prefer text-based information or may be indifferent to format altogether. For content creators and health communicators, this finding argues for audience-aware production strategies, in which the choice of media format is calibrated to the target population rather than assumed to be universally beneficial.

The broader implications of the work reach into platform governance, health communication practice, and future research. By identifying a structured set of evaluation indicators and organizing them into five dimensions, the researchers have given platform operators, public health agencies, and information quality auditors a user-validated yardstick for assessing the health content that flows through mobile social media. Moreover, the demonstration that perceptions vary partially across user groups leads the authors to a clear conclusion: future optimization of health information quality can be carried out in a targeted manner for different user groups. Rather than applying uniform quality interventions, platforms and health authorities could tailor their efforts, strengthening, for example, credibility signals for all users while adjusting multimedia presentation strategies for specific segments.

The study, conducted in accordance with the Declaration of Helsinki with ethical approval from the Ethics Committee of the Second Xiangya Hospital of Central South University and informed consent from all participants, was supported by grants from the 2023 Key R&D Projects in Hunan Province and a 2023 Teaching Reform Research Project of Ordinary Higher Education Institutions in Hunan Province. As health misinformation continues to spread at the speed of a share button, frameworks like this one shift the conversation from vague alarm about bad information to concrete, measurable criteria grounded in what users themselves expect. The message for the digital health era is clear: quality evaluation must live where the information lives, on the mobile screens of the people it is meant to protect.

Subject of Research: User-perception-based evaluation indicators for health information quality on mobile social media

Article Title: Research on health information quality evaluation indicators for mobile social media based on user perception

Article References: Luo, A., Xu, L., Xu, Q., Nie, Y., Hu, D., Xin, Z., Dai, P., Zhao, X., Pan, Z., & Dai, P. (2026). Research on health information quality evaluation indicators for mobile social media based on user perception. BMC Public Health. https://doi.org/10.1186/s12889-026-29799-4

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29799-4

Keywords: mobile social media, health information quality, evaluation indicators, user perception, public health, health informatics, hierarchical clustering, health communication, e-health, misinformation, personalized recommendation, BMC Public Health