title: Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study.
abstract: With a lifetime prevalence of 16.2%, major depressive disorder is the fifth biggest contributor to the disease burden in the United States.
  
  
  
  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).
  
  
  
  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.
  
  
  
  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).
  
  
  
  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.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
authors.forename: Danielle
authors.identifier: 0000-0003-3802-4457
authors.initials: D
authors.lastname: Mowery
authors.affiliation: Department of Psychology, University of Utah, Salt Lake City, UT, United States.
authors.forename: Hilary
authors.identifier: 0000-0002-1963-6266
authors.initials: H
authors.lastname: Smith
authors.affiliation: Department of Psychology, University of Utah, Salt Lake City, UT, United States.
authors.forename: Tyler
authors.identifier: 0000-0002-1846-0887
authors.initials: T
authors.lastname: Cheney
authors.affiliation: Department of Family And Preventive Medicine, University of Utah, Salt Lake City, UT, United States.
authors.forename: Greg
authors.identifier: 0000-0002-6292-276X
authors.initials: G
authors.lastname: Stoddard
authors.affiliation: Qntfy, Crownsville, MD, United States.|Human Language Technology Center of Excellence, John Hopkins University, Baltimore, MD, United States.
authors.forename: Glen
authors.identifier: 0000-0002-3494-7388
authors.initials: G
authors.lastname: Coppersmith
authors.affiliation: Department of Psychology, University of Utah, Salt Lake City, UT, United States.
authors.forename: Craig
authors.identifier: 0000-0002-9714-0733
authors.initials: C
authors.lastname: Bryan
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
authors.forename: Mike
authors.identifier: 0000-0002-3209-8108
authors.initials: M
authors.lastname: Conway
mesh_terms: D003863:Depression / Q000175:diagnosis* / Q000453:epidemiology; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000453:epidemiology; D006801:Humans; D020407:Internet / Q000706:statistics & numerical data*; D000069550:Machine Learning; D011584:Psychology; D061108:Social Media / Q000706:statistics & numerical data*; D013315:Stress, Psychological / Q000175:diagnosis* / Q000453:epidemiology
keywords: Twitter messaging; data annotation; machine learning; major depressive disorder; natural language processing; social media