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

Theme issue 2019: Semantics of Mental Health (5) Artificial Intelligence (827) Machine Learning (1122) Natural Language Processing (535)

Published on in Vol 8 , No 10 (2020) :October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18273, first published .
Exploring Eating Disorder Topics on Twitter: Machine Learning Approach

Exploring Eating Disorder Topics on Twitter: Machine Learning Approach

Exploring Eating Disorder Topics on Twitter: Machine Learning Approach

Authors of this article:

Sicheng Zhou 1 Author Orcid Image ;   Yunpeng Zhao 2 Author Orcid Image ;   Jiang Bian 2 Author Orcid Image ;   Ann F Haynos 3 Author Orcid Image ;   Rui Zhang 1, 4 Author Orcid Image
Article Authors Cited by (14) Tweetations (4) Metrics

Journals

  1. 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
  2. Chandrasekaran R, Desai R, Shah H, Kumar V, Moustakas E. Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts. JMIR Infodemiology 2022;2(1):e33909 View
  3. Fardouly J, Crosby R, Sukunesan S. Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions. Journal of Eating Disorders 2022;10(1) View
  4. Liang D, Frederick D, Lledo E, Rosenfield N, Berardi V, Linstead E, Maoz U. Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I. Body Image 2022;41:32 View
  5. Zhang T, Schoene A, Ji S, Ananiadou S. Natural language processing applied to mental illness detection: a narrative review. npj Digital Medicine 2022;5(1) View
  6. Singh T, Roberts K, Cohen T, Cobb N, Franklin A, Myneni S. Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework. Journal of Biomedical Informatics 2023;140:104324 View
  7. Benítez-Andrades J, García-Ordás M, Russo M, Sakor A, Fernandes Rotger L, Vidal M, Kondylakis H, Rao P, Stefanidis K, Stefanidis K, Kondylakis H, Rao P. Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts. Semantic Web 2023;14(5):873 View
  8. Chandrasekaran R, Bapat P, Jeripity Venkata P, Moustakas E. Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews. Telemedicine and e-Health 2023 View
  9. Schmälzle R, Wilcox S. Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine. Journal of Medical Internet Research 2022;24(1):e28858 View
  10. Yang K, Zhang T, Ananiadou S. A mental state Knowledge–aware and Contrastive Network for early stress and depression detection on social media. Information Processing & Management 2022;59(4):102961 View
  11. Park S, Mahlobo C, Oliver Peets J. Flourishing through traveling while Black: Unfiltered voices of Black travelers. Tourism Management 2022;91:104514 View
  12. Lookingbill V, Mohammadi E, Cai Y. Assessment of Accuracy, User Engagement, and Themes of Eating Disorder Content in Social Media Short Videos. JAMA Network Open 2023;6(4):e238897 View