JMIR Publications

ABSTRACT

Background:

The vaccination uptake rates of 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 address the 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:

The 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 concluded the convolutional neural network (CNN) model outperformed all other models in identifying tweets containing false HPV-vaccine related information (F1=91.95). We develop a completely unsupervised causal mining method to identify HPV vaccine candidate effects with a recall of (0.54) when considering proper matches and discover over 1900 novel candidate effect phrases. Furthermore, we show that the misinformation messages are framed about the potential risk of vaccines while truthful messages describe the potential benefits of the vaccines.

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.