Published on in Vol 20, No 10 (2018): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11515, first published .
Nature and Diffusion of Gynecologic Cancer–Related Misinformation on Social Media: Analysis of Tweets

Nature and Diffusion of Gynecologic Cancer–Related Misinformation on Social Media: Analysis of Tweets

Nature and Diffusion of Gynecologic Cancer–Related Misinformation on Social Media: Analysis of Tweets

Authors of this article:

Liang Chen 1 Author Orcid Image ;   Xiaohui Wang 2 Author Orcid Image ;   Tai-Quan Peng 3 Author Orcid Image

Journals

  1. Walter N, Brooks J, Saucier C, Suresh S. Evaluating the Impact of Attempts to Correct Health Misinformation on Social Media: A Meta-Analysis. Health Communication 2020:1 View
  2. Leung R. Increasing the Impact of JMIR Journals in the Attention Economy. Journal of Medical Internet Research 2019;21(10):e16172 View
  3. Pagoto S, Waring M, Xu R. A Call for a Public Health Agenda for Social Media Research. Journal of Medical Internet Research 2019;21(12):e16661 View
  4. Gao S, He L, Chen Y, Li D, Lai K. Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media. Journal of Medical Internet Research 2020;22(7):e16649 View
  5. Xu Q, Chen S, Safarnejad L. Effects of Information Veracity and Message Frames on Information Dissemination: A Case Study of 2016 Zika Epidemic Discussion on Twitter. Health Communication 2020:1 View
  6. Tao Z, Chu G, McGrath C, Hua F, Leung Y, Yang W, Su Y. Nature and Diffusion of COVID-19–related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo. Journal of Medical Internet Research 2020;22(6):e19981 View
  7. Smaldone F, Ippolito A, Ruberto M. The shadows know me: Exploring the dark side of social media in the healthcare field. European Management Journal 2020;38(1):19 View
  8. Zhao Y, Da J, Yan J. Detecting health misinformation in online health communities: Incorporating behavioral features into machine learning based approaches. Information Processing & Management 2021;58(1):102390 View
  9. Xu Q, Song Y, Yu N, Chen S. Are you passing along something true or false? Dissemination of social media messages about genetically modified organisms. Public Understanding of Science 2021;30(3):285 View
  10. Au C, Ho K, Chiu D. Stopping healthcare misinformation: The effect of financial incentives and legislation. Health Policy 2021;125(5):627 View
  11. Johnston E, Pols J, Ekberg S. Needs, preferences, and experiences of adult cancer survivors in accessing dietary information post‐treatment: A scoping review. European Journal of Cancer Care 2021;30(2) View
  12. Lanius C, Weber R, MacKenzie W. Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey. Social Network Analysis and Mining 2021;11(1) View
  13. Pan W, Liu D, Fang J. An Examination of Factors Contributing to the Acceptance of Online Health Misinformation. Frontiers in Psychology 2021;12 View
  14. Griffin A, Topaloglu U, Davis S, Chung A. From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care. Yearbook of Medical Informatics 2020;29(01):235 View
  15. Wilner T, Holton A. Breast Cancer Prevention and Treatment: Misinformation on Pinterest, 2018. American Journal of Public Health 2020;110(S3):S300 View
  16. Warner E, Waters A, Cloyes K, Ellington L, Kirchhoff A. Young adult cancer caregivers' exposure to cancer misinformation on social media. Cancer 2021;127(8):1318 View

Books/Policy Documents

  1. Khalid F, Rehman S, Shafique M. Security of Cyber-Physical Systems. View
  2. Wang X, Han Y, Leung V, Niyato D, Yan X, Chen X. Edge AI. View