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

Infodemiology and Infoveillance (1349) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Depression and Mood Disorders; Suicide Prevention (1363) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 20, No 5 (2018): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9267, first published .
Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates

Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates

Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates

Authors of this article:

Elizabeth M Seabrook1 Author Orcid Image ;   Margaret L Kern2 Author Orcid Image ;   Ben D Fulcher1 Author Orcid Image ;   Nikki S Rickard1, 2 Author Orcid Image
Article Authors Cited by (69) Tweetations (22) Metrics

Elizabeth M Seabrook   1 , BPsych (Hons) ;   Margaret L Kern   2 , PhD ;   Ben D Fulcher   1 , PhD ;   Nikki S Rickard   1, 2 , PhD

1 Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, Australia

2 Centre for Positive Psychology, The Melbourne Graduate School of Education, The University of Melbourne, Melbourne, Australia

Corresponding Author:

  • Elizabeth M Seabrook, BPsych (Hons)
  • Monash Institute of Cognitive and Clinical Neurosciences
  • School of Psychological Sciences
  • Monash University
  • 18 Innovation Walk, Clayton Campus
  • Melbourne, 3800
  • Australia
  • Phone: 61 0413170668
  • Email: elizabeth.seabrook@monash.edu