ArticleVirology journal2026
Mapping the global research landscape on HPV vaccine hesitancy: a machine-learning based bibliometric analysis.
Article in Virology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Policy events, emotional fluctuations, and vaccination behavior: a longitudinal analysis of public opinion on HPV vaccines in China.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHPV vaccination remains the most effective strategy for cervical cancer prevention, yet hesitancy impedes global uptake. Given the rapidly expanding and fragmented literature, traditional reviews struggle to capture dynamic thematic shifts. This study utilized a machine-learning-enhanced bibliometric approach to map the knowledge domain and identify evolutionary trends in HPV vaccine hesitancy research.
methodsWe analyzed publications retrieved from the Web of Science Core Collection (WoSCC) between 2011 and 2025. VOSviewer, CiteSpace, and Bibliometrix were employed to construct co-authorship and citation networks. Furthermore, we applied Latent Dirichlet Allocation (LDA), a probabilistic topic modeling algorithm, to conduct an unsupervised analysis of abstract texts, enabling the detection of latent semantic themes and their temporal evolution.
resultsA total of 711 publications were identified, showing an exponential growth trajectory since 2017. The United States contributed over half of the global output (53.4%), revealing a geographical imbalance compared to low- and middle-income countries (LMICs). LDA modeling unveiled three distinct thematic clusters: (1) determinants of adolescent vaccination and parental decision-making; (2) public health strategies for improving uptake and knowledge; and (3) the disrupting impact of social media and COVID-19-related misinformation. While early research focused on “access” and “safety”, post-2020 topics have heavily shifted towards “infodemics” and “trust”.
conclusionThe research landscape on HPV vaccine hesitancy was characterized by significant geographic disparities, with high-income countries dominating the discourse despite the higher disease burden in LMICs. The application of LDA revealed a stagnation in traditional barrier studies and a critical pivot towards digital information ecosystems. These findings highlighted a disconnect between scholarly output and practical vaccination coverage, underscoring the need for research to pivot from descriptive surveys to intervention-based studies in underrepresented regions.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.