TY  - JOUR
AU  - Huang, Liang-Chin
AU  - Eiden, Amanda L
AU  - He, Long
AU  - Annan, Augustine
AU  - Wang, Siwei
AU  - Wang, Jingqi
AU  - Manion, Frank J
AU  - Wang, Xiaoyan
AU  - Du, Jingcheng
AU  - Yao, Lixia
PY  - 2024
DA  - 2024/6/21
TI  - Natural Language Processing–Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation
JO  - JMIR Med Inform
SP  - e57164
VL  - 12
KW  - vaccine sentiment
KW  - vaccine hesitancy
KW  - natural language processing
KW  - NLP
KW  - social media
KW  - social media platforms
KW  - real-time tracking
KW  - vaccine
KW  - vaccines
KW  - sentiment
KW  - sentiments
KW  - vaccination
KW  - vaccinations
KW  - hesitancy
KW  - attitude
KW  - attitudes
KW  - opinion
KW  - perception
KW  - perceptions
KW  - perspective
KW  - perspectives
KW  - machine learning
KW  - uptake
KW  - willing
KW  - willingness
KW  - classification
AB  - Background: Vaccines serve as a crucial public health tool, although 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: This study aimed to create a real-time, natural language processing (NLP)–based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms. Methods: We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; 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 (confidence, complacency, and convenience) hesitancy model, conceptualizing an online 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 from 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 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2024/1/e57164
UR  - https://doi.org/10.2196/57164
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38904984
DO  - 10.2196/57164
ID  - info:doi/10.2196/57164
ER  -