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

Methods and New Tools in Mental Health Research (463) Natural Language Processing (706) Machine Learning (1501) Mass Media/Social Media Communication and Campaigns (466) Infodemiology and Infoveillance (1369) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1565) Infoveillance and Social Listening (214) Users' and Patients' Needs for Mental Health Services (409)

Published on in Vol 9, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33685, first published .
Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study

Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study

Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study

Authors of this article:

Anne Marie Stupinski1 Author Orcid Image ;   Thayer Alshaabi1, 2 Author Orcid Image ;   Michael V Arnold1 Author Orcid Image ;   Jane Lydia Adams1, 3 Author Orcid Image ;   Joshua R Minot1 Author Orcid Image ;   Matthew Price4 Author Orcid Image ;   Peter Sheridan Dodds1, 5 Author Orcid Image ;   Christopher M Danforth1, 6 Author Orcid Image
Article Authors Cited by (9) Tweetations (19) Metrics

Journals

  1. Foulkes L, Andrews J. Are mental health awareness efforts contributing to the rise in reported mental health problems? A call to test the prevalence inflation hypothesis. New Ideas in Psychology 2023;69:101010 View
  2. Cahill A, Carlson J. A Scoping Review of Physician Advocacy on Twitter. Cureus 2023 View
  3. Madden E, Prior K, Guckel T, Garlick Bock S, Bryant Z, O’Dean S, Nepal S, Ward C, Thornton L. “What Do I Say? How Do I Say it?” Twitter as a Knowledge Dissemination Tool for Mental Health Research. Journal of Health Communication 2024;29(1):20 View
  4. Tudehope L, Harris N, Vorage L, Sofija E. What methods are used to examine representation of mental ill-health on social media? A systematic review. BMC Psychology 2024;12(1) View
  5. Song Y, Xu R, Huang Y, Ni S, Fan Y. Assessing the Interplay Between Public Attention and Government Responsiveness With Digital Trace Data: Navigating Leadership and Followership in China’s COVID-19 Vaccination Campaign. Social Science Computer Review 2024 View
  6. Ansell M, Finlay-Jones A, Bayliss D, Ohan J. “It Just Makes You Feel Horrible”: A Thematic Analysis of the Stigma Experiences of Youth with Anxiety and Depression. Journal of Child and Family Studies 2024;33(7):2121 View
  7. Yang L, Tan X, Lang R, Wang T, Li K. Reliability and validity of the Chinese version of the doomscrolling scale and the mediating role of doomscrolling in the bidirectional relationship between insomnia and depression. BMC Psychiatry 2024;24(1) View
  8. Kishore S, Sundaram D, Myers M. A temporal dynamics framework and methodology for computationally intensive social media research. Journal of Information Technology 2024 View
  9. O'Brien G, Ganjigunta R, Dhillon P. Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational Study. Journal of Medical Internet Research 2024;26:e56651 View