Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media
Year: 2019
Type: article
Abstract: Our aim was to characterize health beliefs about the human papillomavirus (HPV) vaccine in a large set of Twitter posts (tweets). We collected a Twitter data set related to the HPV vaccine from 1 January 2014, to 31 December 2017. We proposed a deep-learning-based framework to mine health beliefs on the HPV vaccine from Twitter. Deep learning achieved high performance in terms of sensitivity, specificity, and accuracy. A retrospective analysis of health beliefs found that HPV vaccine beliefs may be evolving on Twitter.
Source: npj Digital Medicine
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,680
Funders: National Science Foundation, U.S. Department of Health and Human Services, Cancer Prevention and Research Institute of Texas, University of Texas Health Science Center at Houston, National Institutes of Health, National Institute of Allergy and Infectious Diseases, U.S. National Library of Medicine
Awards: G142613954, G1802251166, G200861231, G3526034163, G3909241516, Dynamic learning for post-vaccine event prediction using temporal information in VAERS, Biomedical Computing and Informatics Strategies for Infectious Disease Research, Machine Learning Prediction of Cancer Susceptibility, A Person-Centric Prediction Model of Job Loss based on Social Media, Collaborative Training of a New Cadre of Innovative Cancer Prevention Researchers, Patient Medical History Representation, Extraction, and Inference from EHR Data, G9000020520, G938789427
OpenAlex ID: W2937972539