Identifying False HPV-Vaccine Information and Modeling Its Impact on Risk Perceptions

Human papillomavirus (HPV) is the most common sexually transmitted infection in the U.S., with over 34,000 new HPV-related cancers diagnosed annually, according to the Centers for Disease Control and Prevention. An HPV vaccine, which was approved by the Food and Drug Administration (FDA) in 2006, is recommended as part of routine vaccinations for school-aged children. However, the vaccine's uptake remains low in part because of incorrect perceptions of vaccination risks, which has been linked to the spread of false information about the vaccine.

For this project, Chin and her collaborators from the University of Illinois at Chicago and University of Chicago will build a model to identify false HPV-vaccine information on Twitter and demonstrate its impact on individual risk perceptions. They 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).

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