Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media

Jingcheng Du; Rachel M Cunningham; Yang Xiang; Fang Li; Yuxi Jia; Julie A Boom; Sahiti Myneni; Jiang Bian; Chongliang Luo; Yong Chen; Cui Tao

doi:10.1038/s41746-019-0102-4

Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media

NPJ Digit Med. 2019 Apr 15:2:27. doi: 10.1038/s41746-019-0102-4. eCollection 2019.

Authors

Jingcheng Du¹, Rachel M Cunningham², Yang Xiang¹, Fang Li¹, Yuxi Jia^{1

3}, Julie A Boom^{2

4}, Sahiti Myneni¹, Jiang Bian⁵, Chongliang Luo⁶, Yong Chen^{6

7

8}, Cui Tao¹

Affiliations

¹ 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA.
² 2Texas Children's Hospital, Houston, TX USA.
³ 3School of Public Health, Jilin University, Changchun, China.
⁴ 4Baylor College of Medicine, Houston, TX USA.
⁵ 5Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL USA.
⁶ 6Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA USA.
⁷ 7Institute for Biomedical Informatics, The University of Pennsylvania, Philadelphia, PA USA.
⁸ 8Center for Evidence-based Practice, The University of Pennsylvania, Philadelphia, PA USA.

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.

Keywords: Cancer prevention; Health care.

Abstract

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