Deep learning analysis of trajectories and spatial variations in HPV vaccine discussions on Chinese Weibo.

BACKGROUND

Since 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.

METHODS

We 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.

RESULTS

Among 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 = 3.02×10-11), negative attitudes decline from a peak of 20.3% to 5.5% (P = 1.28×10-5), and misinformation declines from 36.6% to 10.7% (P = 1.33×10-6). Central regions of China exhibit a higher prevalence of positive attitudes, whereas Beijing, Shanghai and northeastern regions show higher prevalence of negative attitudes and misinformation. Positive attitudes are significantly lower for 2-valent vaccines (65.7%) than 4-valent (79.6%; P = 0.0005) or 9-valent vaccines (74.1%; P = 0.0005).

CONCLUSIONS

Social 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.