||5R21CA237483-02 Interpret this number
||University Of Illinois At Urbana-Champaign
||Identifying False HPV-Vaccine Information and Modeling Its Impact on Risk Perceptions
Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States, with over
30,000 new HPV-related-cancers are diagnosed annually. Although HPV vaccines have been approved by the
Food and Drug Administration (FDA) since 2006 and recommended for routine vaccination for school-age girls
and boys, vaccination rates remain low. One reason that has contributed to low vaccination rates is incorrect
“risk perceptions” around HPV vaccines such as the high perceived risks of adverse events or side effects from
the HPV vaccine. Incorrect risk perceptions are often rooted in the false information about HPV vaccines that
people are exposed to in their daily life, including social media. The impact of social media on health information
is substantial. Negative social-media HPV-vaccine information has been found to have an association with low
vaccination coverage. Given the negative consequences of false information, there is a need to develop a robust
and scalable way to detect false HPV-vaccine information before it propagates and negatively impacts behavior.
The overarching goal of the proposed research is to build a model to identify false HPV-vaccine information on
Twitter, demonstrate its impact on individual risk perceptions and measure its underlying mechanisms on risk
perception formation. We propose a novel approach to leverage machine learning, natural language processing,
network analysis, crowdsourcing/expert data annotation, psycholinguistic analysis and statistical modeling to
investigate the false HPV-vaccine information collectively (in terms of its detection and propagation patterns)
and individually (in terms of its impact and underlying cognitive mechanisms). Our study will first build a
computational model to detect false HPV-vaccine information on Twitter. By modeling the domain-specific HPV-
vaccine related text content, information-veracity related linguistic features, individual and collective user
behaviors, and dissemination patterns, our model will be able to detect false HPV-vaccine information before it
gets verified and spreads widely. We will then investigate the impact of false HPV-vaccine information on risk
perceptions around HPV vaccination operationalized by natural language processing methods and a developed
HPV-vaccine Risk Lexicon. We will further conduct psycholinguistic analysis on the false HPV-vaccine
information and use statistical modeling to uncover the underlying mechanism of risk perceptions. Our study will
make a critical and timely contribution to identifying the false HPV-vaccine information and its impact, which has
the potential to be applied to other health topics. This proposed project will also address the National Cancer
Institute priorities in promoting HPV vaccines and combating misinformation in cancer prevention and control.
Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models.
, Morales A.
, Lourentzou I.
, Caskey R.
, Liu B.
, Schwartz A.
, Chin J.
Journal of medical Internet research, 2021-09-09; 23(9), p. e30451.