Published on in Vol 20, No 12 (2018): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11817, first published .
Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram

Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram

Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram

Journals

  1. Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine 2020;3(1) View
  2. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  3. Rousidis D, Koukaras P, Tjortjis C. Social media prediction: a literature review. Multimedia Tools and Applications 2020;79(9-10):6279 View
  4. Hayes A, Andrews L. A complex systems approach to the study of change in psychotherapy. BMC Medicine 2020;18(1) View
  5. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  6. Graham S, Depp C, Lee E, Nebeker C, Tu X, Kim H, Jeste D. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Current Psychiatry Reports 2019;21(11) View
  7. Ávila-Tomás J, Mayer-Pujadas M, Quesada-Varela V. La inteligencia artificial y sus aplicaciones en medicina II: importancia actual y aplicaciones prácticas. Atención Primaria 2021;53(1):81 View
  8. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  9. Marsch L, Campbell A, Campbell C, Chen C, Ertin E, Ghitza U, Lambert-Harris C, Hassanpour S, Holtyn A, Hser Y, Jacobs P, Klausner J, Lemley S, Kotz D, Meier A, McLeman B, McNeely J, Mishra V, Mooney L, Nunes E, Stafylis C, Stanger C, Saunders E, Subramaniam G, Young S. The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network. Journal of Substance Abuse Treatment 2020;112:4 View
  10. Gupta R, Ariefdjohan M. Mental illness on Instagram: a mixed method study to characterize public content, sentiments, and trends of antidepressant use. Journal of Mental Health 2020:1 View
  11. Marsch L. Digital health data-driven approaches to understand human behavior. Neuropsychopharmacology 2021;46(1):191 View
  12. Wang X, Chen S, Li T, Li W, Zhou Y, Zheng J, Chen Q, Yan J, Tang B. Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis. JMIR Medical Informatics 2020;8(7):e17958 View
  13. Kim J, Uddin Z, Lee Y, Nasri F, Gill H, Subramanieapillai M, Lee R, Udovica A, Phan L, Lui L, Iacobucci M, Mansur R, Rosenblat J, McIntyre R. A Systematic review of the validity of screening depression through Facebook, Twitter, Instagram, and Snapchat. Journal of Affective Disorders 2021;286:360 View
  14. Stirling E, Willcox J, Ong K, Forsyth A. Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours. Public Health Nutrition 2021;24(6):1193 View
  15. Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Mental Health 2021;8(6):e24668 View