TY  - JOUR
AU  - Tomaszewski, Tre
AU  - Morales, Alex
AU  - Lourentzou, Ismini
AU  - Caskey, Rachel
AU  - Liu, Bing
AU  - Schwartz, Alan
AU  - Chin, Jessie
PY  - 2021
DA  - 2021/9/9
TI  - Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models
JO  - J Med Internet Res
SP  - e30451
VL  - 23
IS  - 9
KW  - misinformation
KW  - disinformation
KW  - social media
KW  - HPV
KW  - human papillomavirus vaccination
KW  - vaccination
KW  - causality mining
KW  - cause
KW  - effect
KW  - risk perceptions
KW  - vaccine
KW  - perception
KW  - risk
KW  - Twitter
KW  - machine learning
KW  - natural language processing
KW  - cervical cancer
AB  - Background: The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy. Objective: Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media. Methods: This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine–related information on Twitter. Results: We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine–related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary. Conclusions: Our research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/9/e30451
UR  - https://doi.org/10.2196/30451
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34499043
DO  - 10.2196/30451
ID  - info:doi/10.2196/30451
ER  -