Open AccessUnderstanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study
Author(s) -
Danielle L. Mowery,
Hilary A. Smith,
Tyler Cheney,
Greg Stoddard,
Glen Coppersmith,
Craig J. Bryan,
Mike Conway
Publication year - 2017
Publication title -
jmir. journal of medical internet research/journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/jmir.6895
Subject(s) - psychosocial , psychomotor retardation , stressor , depression (economics) , mood , major depressive episode , psychology , major depressive disorder , psychiatry , clinical psychology , medicine , alternative medicine , pathology , economics , macroeconomics
Background With a lifetime prevalence of 16.2%, major depressive disorder is the fifth biggest contributor to the disease burden in the United States. Objective The aim of this study, building on previous work qualitatively analyzing depression-related Twitter data, was to describe the development of a comprehensive annotation scheme (ie, coding scheme) for manually annotating Twitter data with Diagnostic and Statistical Manual of Mental Disorders, Edition 5 (DSM 5) major depressive symptoms (eg, depressed mood, weight change, psychomotor agitation, or retardation) and Diagnostic and Statistical Manual of Mental Disorders, Edition IV (DSM-IV) psychosocial stressors (eg, educational problems, problems with primary support group, housing problems). Methods Using this annotation scheme, we developed an annotated corpus, Depressive Symptom and Psychosocial Stressors Acquired Depression, the SAD corpus, consisting of 9300 tweets randomly sampled from the Twitter application programming interface (API) using depression-related keywords (eg, depressed, gloomy, grief). An analysis of our annotated corpus yielded several key results. Results First, 72.09% (6829/9473) of tweets containing relevant keywords were nonindicative of depressive symptoms (eg, “we’re in for a new economic depression”). Second, the most prevalent symptoms in our dataset were depressed mood and fatigue or loss of energy. Third, less than 2% of tweets contained more than one depression related category (eg, diminished ability to think or concentrate, depressed mood). Finally, we found very high positive correlations between some depression-related symptoms in our annotated dataset (eg, fatigue or loss of energy and educational problems; educational problems and diminished ability to think). Conclusions We successfully developed an annotation scheme and an annotated corpus, the SAD corpus, consisting of 9300 tweets randomly-selected from the Twitter application programming interface using depression-related keywords. Our analyses suggest that keyword queries alone might not be suitable for public health monitoring because context can change the meaning of keyword in a statement. However, postprocessing approaches could be useful for reducing the noise and improving the signal needed to detect depression symptoms using social media.