Abstract

BackgroundThe 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.ObjectiveGiven 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.MethodsThis 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.ResultsWe 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.ConclusionsOur research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media.

Highlights

  • About 13,000 women are newly diagnosed with invasive cervical cancer and over 4000 women die from it every year [1]

  • We experimented with Wikipedia-pretrained embeddings and without any pretrained embeddings: our experiments showed that the model performed better in terms of accuracy when initialized with human papillomavirus (HPV)-related pretrained word embeddings

  • The second stage was to extract layman language about the descriptions of risk perceptions based on the user-generated content by 2 trained annotators (interrater reliability: Cohen's kappa coefficient (κ)=0.80)

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Summary

Introduction

About 13,000 women are newly diagnosed with invasive cervical cancer and over 4000 women die from it every year [1]. Cervical cancer is caused by certain types of human papillomavirus (HPV) [2,3]. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. 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. We developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV 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

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