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

npj Digital Medicine

Volumn 2, Issue 1, 2019, Pages

  Du, Jingcheng ^a   Cunningham, Rachel M ^b   Xiang, Yang ^a   Li, Fang ^a   Jia, Yuxi ^a,c   Boom, Julie A ^b,d   Myneni, Sahiti ^a   Bian, Jiang ^e   Luo, Chongliang ^f   Chen, Yong ^f,g   Tao, Cui ^a  

^a UNIVERSITY OF TEXAS   (United States)

^b TEXAS CHILDREN S HOSPITAL   (United States)

^c JILIN UNIVERSITY   (China)

^d BAYLOR COLLEGE OF MEDICINE   (United States)

^e UNIVERSITY OF FLORIDA COLLEGE OF MEDICINE   (United States)

^f UNIVERSITY OF PENNSYLVANIA   (United States)

^g UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP LEARNING; SOCIAL NETWORKING (ONLINE);

DATA SET; HUMAN PAPILLOMA VIRUS; PERFORMANCE; RETROSPECTIVE ANALYSIS; SOCIAL MEDIA; TWITTER POSTS;

VACCINES;

WART VIRUS VACCINE;

ARTICLE; DEEP LEARNING; HEALTH BELIEF; HUMAN; PREVALENCE; PRIORITY JOURNAL; SOCIAL MEDIA; TEMPORAL ANALYSIS; WART VIRUS;

EID: 85135598435     PISSN: None     EISSN: 23986352     Source Type: Journal    
DOI: 10.1038/s41746-019-0102-4     Document Type: Article

References (19)

1. Gilkey, M. B., Calo, W. A., Marciniak, M. W. & Brewer, N. T. Parents who refuse or delay HPV vaccine: differences in vaccination behavior, beliefs, and clinical communication preferences. Hum. Vaccin. Immunother. 13, 680–686 (2017).
2. Larson, H. J. et al. Measuring vaccine confidence: analysis of data obtained by a media surveillance system used to analyse public concerns about vaccines. Lancet. Infect. Dis. 13, 606–613 (2013).
3. Rosenstock, I. M. The health belief model and preventive health behavior. Health Educ. Monogr. 2, 354–386 (1974).
4. Reiter, P. L., Brewer, N. T., Gottlieb, S. L., McRee, A.-L. & Smith, J. S. Parents’ health beliefs and HPV vaccination of their adolescent daughters. Soc. Sci. Med. 69, 475–480 (2009).
5. Donadiki, E. M. et al. Health belief model applied to non-compliance with HPV vaccine among female university students. Public Health 128, 268–273 (2014).
6. Skinner, C. S., Tiro, J. & Champion, V. L. The health belief model. In: Glanz K., Rimer, B. K., Viswanath, K., eds. Health Behavior: Theory, Research, and Practice. 5th ed. 75-94 (Jossey-Bass, San Francisco, 2015).
7. Mitra, T., Counts, S. & Pennebaker, J. Understanding Anti-Vaccination Attitudes in Social Media. In Proc. of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016) 269–278 (Cologne, Germany, 2016).
8. Chan, B., Lopez, A. & Sarkar, U. The canary in the coal mine tweets: social media reveals public perceptions of non-medical use of opioids. PLoS ONE 10, 1–10 (2015).
9. Kagashe, I., Yan, Z. & Suheryani, I. Enhancing seasonal influenza surveillance: topic analysis of widely used medicinal drugs using Twitter data. J. Med. Internet. Res. 19, 315–3151 (2017).
10. Shapiro, G. K., Surian, D., Dunn, A. G., Perry, R. & Kelaher, M. Comparing human papillomavirus vaccine concerns on Twitter: a cross-sectional study of users in Australia, Canada and the UK. BMJ Open 7, e016869 (2017).
11. Du, J., Xu, J., Song, H.-Y. & Tao, C. Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data. BMC Med. Inform. Decis. Mak. 17, 69 (2017).
12. Dunn, A. G., Leask, J., Zhou, X., Mandl, K. D. & Coiera, E. Associations between exposure to and expression of negative opinions about human papillomavirus vaccines on social media: an observational study. J. Med. Internet. Res. 17, e144 (2015).
13. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
14. Young, T., Hazarika, D., Poria, S. & Cambria, E. Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag. 13, 55–75 (2018).
15. Tang, D., Wei, F., Qin, B., Liu, T. & Zhou, M. Coooolll: A Deep Learning System for Twitter Sentiment Classification. In Proc. of the 8th International Workshop on Semantic Evaluation (SemEval–2014), 208–212 (Dublin, Ireland, 2014).
16. Du, J. et al. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Med. Inform. Decis. Mak. 18, 43 (2018).
17. Pennington, J., Socher, R. & Manning, C. D. GloVe: Global Vectors for Word Representation. Empirical Methods in Natural Language Processing (EMNLP). https://nlp.stanford.edu/projects/glove/. 2014. Accessed 3 Jan 2019.
18. Schuster, M. & Paliwal, K. K. Bidirectional recurrent neural networks. IEEE Trans Signal Process. 45, 2673–2681 (1997). Accessed 3 Jan 2019.
19. Zhou, P. et al. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. In Proc. of the 54th Annual Meeting of the Association for Computational Linguistics (Vol. 2: Short Papers) 207–212 (ACL, Berlin, Germany, 2016).