Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models

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dc.contributor.authorTomaszewski, Treen
dc.contributor.authorMorales, Alexen
dc.contributor.authorLourentzou, Isminien
dc.contributor.authorCaskey, Rachelen
dc.contributor.authorLiu, Bingen
dc.contributor.authorSchwartz, Alanen
dc.contributor.authorChin, Jessieen
dc.date.accessioned2022-03-30T12:07:07Zen
dc.date.available2022-03-30T12:07:07Zen
dc.date.issued2021-09-09en
dc.description.abstractBackground: 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.en
dc.description.notesWe thank Chieh-Li (Julian) Chin and Wen-Yuh Su for their assistance in the data collection and preparation for this study. We also thank the Technology Services and the Cancer Center at Illinois at the University of Illinois at Urbana-Champaign for their support of our study. This study was supported by a grant from the National Institutes of Health (#1R21CA237483). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Institutes of Health.en
dc.description.sponsorshipTechnology Services and the Cancer Center at Illinois at the University of Illinois at Urbana-Champaign; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [1R21CA237483]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.2196/30451en
dc.identifier.issn1438-8871en
dc.identifier.issue9en
dc.identifier.othere30451en
dc.identifier.pmid34499043en
dc.identifier.urihttp://hdl.handle.net/10919/109499en
dc.identifier.volume23en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectmisinformationen
dc.subjectdisinformationen
dc.subjectsocial mediaen
dc.subjectHPVen
dc.subjecthuman papillomavirus vaccinationen
dc.subjectvaccinationen
dc.subjectcausality miningen
dc.subjectcauseen
dc.subjecteffecten
dc.subjectrisk perceptionsen
dc.subjectvaccineen
dc.subjectperceptionen
dc.subjectrisken
dc.subjectTwitteren
dc.subjectmachine learningen
dc.subjectnatural language processingen
dc.subjectcervical canceren
dc.titleIdentifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Modelsen
dc.title.serialJournal of Medical Internet Researchen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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