Natural Language Processing–Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation (Preprint)

Abstract

BACKGROUND Vaccines serve as a crucial public health tool, though vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations. OBJECTIVE To create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across three prominent social media platforms. METHODS We mined and curated discussions from Twitter, Reddit, and YouTube social media platforms, posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus (HPV), measles, mumps, and rubella (MMR), and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative, and to classify vaccine hesitancy using the World Health Organization’s (WHO) 3Cs hesitancy model, conceptualizing an interactive dashboard to illustrate and contextualize trends. RESULTS We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines. CONCLUSIONS Our innovative system performs real-time analysis of sentiment and hesitancy on three vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.