ArticleCommunications medicine2026
Deep learning analysis of trajectories and spatial variations in HPV vaccine discussions on Chinese Weibo.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
8 authors.
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Abstract
backgroundSince 2020 China has piloted free human papillomavirus (HPV) vaccinations to address low coverage. We aim to assess the public perceptions, perceived barriers and facilitators towards HPV vaccination in real time, utilizing deep learning-driven social media listening.
methodsWe collected all HPV vaccination discussions on Weibo, a popular Chinese social media platform, made from 2018 to 2023. We annotated 6600 randomly sampled posts manually against behavior change theories, and iteratively fine-tuned and trained deep learning models to auto-annotate all collected posts. Temporal and geographic analyses were conducted regarding public attitudes towards HPV vaccination and their determinants.
resultsAmong 1,972,495 posts identified as relevant to HPV vaccines, deep learning models achieve a predictive accuracy of 0.78 to 0.96. 66.6% and 6.1% of posts contain positive and negative attitudes, respectively. The prevalence of positive attitudes increases from 15.8% to 79.1% (P = 3.02×10
conclusionsSocial media listening represents a promising and economically feasible surveillance approach for timely monitoring of public perceptions of vaccination and a potential tool for policymakers to understand public response to policy adaptation.
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