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

Peer-to-Peer Support and Online Communities (636) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641)

Published on in Vol 19, No 8 (2017): August

Toward Predicting Social Support Needs in Online Health Social Networks

Toward Predicting Social Support Needs in Online Health Social Networks

Toward Predicting Social Support Needs in Online Health Social Networks

Authors of this article:

Min-Je Choi1 Author Orcid Image ;   Sung-Hee Kim2 Author Orcid Image ;   Sukwon Lee3 Author Orcid Image ;   Bum Chul Kwon4 Author Orcid Image ;   Ji Soo Yi5 Author Orcid Image ;   Jaegul Choo1 Author Orcid Image ;   Jina Huh6 Author Orcid Image
Article Authors Cited by (18) Tweetations (11) Metrics

Journals

  1. White B, Giglia R, Scott J, Burns S. How New and Expecting Fathers Engage With an App-Based Online Forum: Qualitative Analysis. JMIR mHealth and uHealth 2018;6(6):e144 View
  2. Zhou J, Liu F, Zhou T. Exploring the Factors Influencing Consumers to Voluntarily Reward Free Health Service Contributors in Online Health Communities: Empirical Study. Journal of Medical Internet Research 2020;22(4):e16526 View
  3. Wang J, Chiu Y, Yu H, Hsu Y. Understanding a Nonlinear Causal Relationship Between Rewards and Physicians’ Contributions in Online Health Care Communities: Longitudinal Study. Journal of Medical Internet Research 2017;19(12):e427 View
  4. Smith C, Levonian Z, Ma H, Giaquinto R, Lein-Mcdonough G, Li Z, O’conner-Von S, Yarosh S. "I Cannot Do All of This Alone". ACM Transactions on Computer-Human Interaction 2020;27(5):1 View
  5. Gareau G. Portraits d’enfance, portraits d’adolescence : pratiques de lecture et pratiques culturelles à travers les étapes du développement humain. Documentation et bibliothèques 2020;66(2):20 View
  6. Wu B. Patient Continued Use of Online Health Care Communities: Web Mining of Patient-Doctor Communication. Journal of Medical Internet Research 2018;20(4):e126 View
  7. Hsu Y, Chiu Y, Wang J, Liu H. Impacts of physician promotion on the online healthcare community: Using a difference-in-difference approach. DIGITAL HEALTH 2022;8:205520762211063 View
  8. Benítez-Andrades J, Alija-Pérez J, Vidal M, Pastor-Vargas R, García-Ordás M. Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study. JMIR Medical Informatics 2022;10(2):e34492 View
  9. Rowan W, O'Connor Y, Lynch L, Heavin C. Comprehension, Perception, and Projection. Journal of Organizational and End User Computing 2021;33(6):1 View
  10. Milios A, Xiong T, McEwan K, McGrath P. Personality, Attitudes, and Behaviors Predicting Perceived Benefit in Online Support Groups for Caregivers: Mixed Methods Study. JMIR Nursing 2022;5(1):e36167 View
  11. Taghvaei N, Masoumi B, Keyvanpour M. Analytical framework for mental health feature extraction methods in social networks. Intelligent Decision Technologies 2021;15(3):343 View
  12. Cao Z, Zheng J, Liu R. Factors Affecting Users’ Continuous Usage in Online Health Communities: An Integrated Framework of SCT and TPB. Healthcare 2023;11(9):1238 View
  13. Pepin K, Bologna F, Thalken R, Wilkens M. Endometriosis Online Communities: How Machine Learning Can Help Physicians Understand What Patients Are Discussing Online. Journal of Minimally Invasive Gynecology 2024 View

Books/Policy Documents

  1. Jang H, Lee Y, Carenini G, Ng R, Campbell G, Ho K. Advances in Artificial Intelligence. View
  2. Ab Aziz A, Rateb R, Bimo A. Advances and Trends in Artificial Intelligence. From Theory to Practice. View