Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates
Elizabeth M Seabrook, Margaret L Kern, Ben D Fulcher, Nikki S Rickard
JOURNAL OF MEDICAL INTERNET RESEARCH | JMIR PUBLICATIONS, INC | Published : 2018
BACKGROUND: Frequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility. OBJECTIVE: The aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates. METHODS: Status updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were co..View full abstract
The authors would like to thank Mehmet Ozmen for his statistical advice in this study. EMS is a recipient of an Australian Government Research Training Program Scholarship.