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Citing this Article

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Published on 13.12.17 in Vol 19, No 12 (2017): December

This paper is in the following e-collection/theme issue:

Works citing "Using Social Media Data to Understand the Impact of Promotional Information on Laypeople’s Discussions: A Case Study of Lynch Syndrome"

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.9266):

(note that this is only a small subset of citations)

  1. Aramburu MJ, Berlanga R, Lanza I. Social Media Multidimensional Analysis for Intelligent Health Surveillance. International Journal of Environmental Research and Public Health 2020;17(7):2289
    CrossRef
  2. Allen CG, Peterson S, Khoury MJ, Brody LC, McBride CM. A scoping review of social and behavioral science research to translate genomic discoveries into population health impact. Translational Behavioral Medicine 2020;
    CrossRef
  3. Ma R, Deng Z, Wu M. Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis. International Journal of Environmental Research and Public Health 2020;17(14):5081
    CrossRef
  4. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023
    CrossRef
  5. Huang M, Zolnoori M, Balls-Berry JE, Brockman TA, Patten CA, Yao L. Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017. Journal of Medical Internet Research 2019;21(4):e13316
    CrossRef
  6. Zhang H, Wheldon C, Dunn AG, Tao C, Huo J, Zhang R, Prosperi M, Guo Y, Bian J. Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States. Journal of the American Medical Informatics Association 2020;27(2):225
    CrossRef
  7. Pérez-Pérez M, Pérez-Rodríguez G, Fdez-Riverola F, Lourenço A. Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics. Journal of Medical Internet Research 2019;21(8):e12610
    CrossRef
  8. Lee EWJ, Yee AZH. Toward Data Sense-Making in Digital Health Communication Research: Why Theory Matters in the Age of Big Data. Frontiers in Communication 2020;5
    CrossRef
  9. Jayaraman PP, Forkan ARM, Morshed A, Haghighi PD, Kang Y. Healthcare 4.0: A review of frontiers in digital health. WIREs Data Mining and Knowledge Discovery 2020;10(2)
    CrossRef
  10. Zhao Y, Guo Y, He X, Wu Y, Yang X, Prosperi M, Jin Y, Bian J. Assessing mental health signals among sexual and gender minorities using Twitter data. Health Informatics Journal 2020;26(2):765
    CrossRef
  11. Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. Journal of the American Medical Informatics Association 2019;26(11):1279
    CrossRef
  12. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
    CrossRef
  13. Lara Ródenas MJD. El voto vigilado. Influencia y control electoral en las hermandades de Huelva durante el Antiguo Régimen. Hispania Sacra 2019;71(144):521
    CrossRef
  14. Du J, Tang L, Xiang Y, Zhi D, Xu J, Song H, Tao C. Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models. Journal of Medical Internet Research 2018;20(7):e236
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.9266):

  1. Zhao Y, Prosperi M, Lyu T, Guo Y, Zhou L, Bian J. Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. 2020. Chapter 30:333
    CrossRef
  2. M. Sergi C. Interactive Multimedia - Multimedia Production and Digital Storytelling. 2019. Chapter 9
    CrossRef
  3. Zhang H, Wheldon C, Tao C, Dunn AG, Guo Y, Huo J, Bian J. Social Web and Health Research. 2019. Chapter 11:207
    CrossRef