Trajectories of and spatial variations in HPV vaccine discussions on Weibo, 2018-2023: a deep learning analysis
Authors: Wang You , Yang Haoyun , Ding Zhijun , Zhou Xinyu , Zhou Yingchen , Ma Liyan , Hou Zhiyuan 
Published: December 2023

SummaryResearch in contextEvidence before this studyWe first searched PubMed for articles published until November 2023 with the keywords “(“HPV”) AND (“Vaccine” or “Vaccination”) AND (“Social Media”)”. We identified about 390 studies, most of which were discussions on the potentials or feasibility of social media in HPV vaccination advocacy or research, or manual coding-driven analyses on text (eg., tweets) about HPV vaccines emerged on social media platforms. When we added keyword “Machine Learning”, we identified only 12 studies, with several of them using AI-driven approach, such as deep learning, machine learning, and natural language process, to analyze extensive text data about public perceptions of HPV vaccination and perform monitor on social media platforms, X (Twitter) and Reddit. All these studies are from English-language social media platforms in developed countries. No study to date has monitored public perceptions of HPV vaccination on social media platforms from the developing countries including China.Added value of this studyThis is the first deep-learning study monitoring public perceptions of HPV vaccination expressed on Chinese social media platforms (Weibo in our case), revealing key temporal and geographic variations. We found a sustained high level of positive attitude towards HPV vaccination and exposure to social norms facilitating HPV vaccination among Weibo users, with a lower national prevalence of negative attitude, perceived barriers to accepting vaccination, misinformation about HPV or HPV vaccination, indicating the achievement of relevant health communication. High prevalence practical barriers to HPV vaccination expressed on Weibo was associated with relatively insufficient of HPV vaccine accessibility in China, suggesting the health systems should prioritize on addressing issues about vaccine supply. Lower positive perception of HPV vaccination among male users, higher vaccine hesitancy towards 2-valent vaccine, and provincial-level spatial cluster of higher negative attitude towards HPV vaccination indicate that tailored strategies need to be formed targeting specific population, areas, and vaccine type. Our monitor practice on public perceptions of HPV vaccine from Weibo shows the feasibility of realizing public health surveillance potential of social media listening in Chinese context. Leveraging recent advances in deep learning, our approach could be a cost-effective supplement to existing surveillance techniques.Implications of all the available evidenceThis national surveillance study highlights the value of deep learning-driven social media listening as a convenient and effective approach for identifying emerging trends in public perceptions of HPV vaccination to inform interventions. As a supplement to existing public health surveillance techniques, it is particularly helpful to inform tailored and timely strategies in health communication and resource allocation at multiple levels. Key stakeholders and officials should maintain focus on health education highlighting the risks and consequences of HPV infections, and benefits and safety of all types of HPV vaccines; health systems should aim to resolve issues of vaccine accessibility. A proposed research area is the further development of deep learning models to monitor public perceptions of vaccines and analyzing misinformation about and barriers to HPV vaccination expressed on Chinese social media platforms.BackgroundHPV vaccination rate is low in China. Understanding the multidimensional barriers and impetuses perceived by individuals to vaccination is essential. We aim to assess the public perceptions, perceived barriers, and facilitators towards HPV vaccination expressed on Chinese social media platform Weibo.MethodsWe collected Weibo posts regarding HPV vaccines between 2018 to 2023. We annotated 6,600 posts manually according to behavior change theories, and subsequently fine-tuned deep learning models to annotate all posts collected. Based on the annotated results of deep learning models, temporal and geographic analyses were conducted for public attitudes towards HPV vaccination and its determinants.FindingsTotally 1,972,495 Weibo posts were identified as relevant to HPV vaccines. Deep learning models reached predictive accuracy of 0.78 to 0.96 in classifying posts. During 2018 to 2023, 1,314,510 (66.6%) posts were classified as positive attitudes. And 224,130 posts (11.4%) were classified as misinformation, 328,442 posts (16.7%) as perceived barriers to accepting vaccines, and 580,590 posts (29.4%) as practical barriers to vaccination. The prevalence of positive attitude increased from 15.8% in March 2018 to 79.1% in mid-2023 (p < 0.001), and misinformation declined from 36.6% in mid-2018 to 10.7% in mid-2023 (P < .001). Central regions exhibited higher prevalence of positive attitudes and social norms, whereas Shanghai, Beijing megacities and northeastern regions showed higher prevalence of negative attitudes and misinformation. Positive attitudes were significantly lower for 2-valent vaccines (65.7%), than 4-valent or 9-valent vaccines (79.6% and 74.1%).InterpretationSocial media listening represents a promising surveillance approach for monitoring public perceptions on health issues and can enable the development of health communication strategies.