TY - JOUR
T1 - Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media
AU - Du, Jingcheng
AU - Cunningham, Rachel M.
AU - Xiang, Yang
AU - Li, Fang
AU - Jia, Yuxi
AU - Boom, Julie A.
AU - Myneni, Sahiti
AU - Bian, Jiang
AU - Luo, Chongliang
AU - Chen, Yong
AU - Tao, Cui
N1 - Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85135598435&partnerID=8YFLogxK
U2 - 10.1038/s41746-019-0102-4
DO - 10.1038/s41746-019-0102-4
M3 - Article
AN - SCOPUS:85135598435
SN - 2398-6352
VL - 2
JO - npj Digital Medicine
JF - npj Digital Medicine
IS - 1
M1 - 27
ER -