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

Formative Evaluation of Digital Health Interventions (2827) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1791) Infodemiology and Infoveillance (1459) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1088) Artificial Intelligence (1745) Machine Learning (1741) Diagnostic Tools in Mental Health (281) Autism Spectrum Disorder (ASD) (130) Infoveillance and Social Listening (252)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52660, first published .
Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

Authors of this article:

Aditi Jaiswal1 Author Orcid Image ;   Peter Washington1 Author Orcid Image
Article Authors Cited by (3) Tweetations (200) Metrics

Journals

  1. Jaiswal A, Shah A, Harjadi C, Windgassen E, Washington P. Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping. JMIR Formative Research 2024;8:e59794 View
  2. Zhang Y, Wang J, Zong H, Singla R, Ullah A, Liu X, Wu R, Ren S, Shen B. The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact. npj Digital Medicine 2025;8(1) View

Conference Proceedings

  1. Carik B, Izaac V, Ding X, Scarpa A, Rho E. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. Reimagining Support: Exploring Autistic Individuals' Visions for AI in Coping with Negative Self-Talk View