@InProceedings{mowery-EtAl:2016:PEOPLES,
  author    = {Mowery, Danielle L  and  Park, Albert  and  Bryan, Craig  and  Conway, Mike},
  title     = {Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health},
  booktitle = {Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {182--191},
  abstract  = {Major depressive disorder, a debilitating and burdensome disease experienced by
	individuals
	worldwide, can be defined by several depressive symptoms (e.g., anhedonia
	(inability to feel
	pleasure), depressed mood, difficulty concentrating, etc.). Individuals often
	discuss their experiences with depression symptoms on public social media
	platforms like Twitter, providing
	a potentially useful data source for monitoring population-level mental health
	risk factors. In a
	step towards developing an automated method to estimate the prevalence of
	symptoms associated with major depressive disorder over time in the United
	States using Twitter, we developed classifiers for discerning whether a Twitter
	tweet represents no evidence of depression or evidence of depression. If there
	was evidence of depression, we then classified whether the tweet contained a
	depressive symptom and if so, which of three subtypes: depressed mood,
	disturbed sleep, or fatigue or loss of energy. We observed that the most
	accurate classifiers could predict classes with high-to-moderate F1-score
	performances for no evidence of depression (85), evidence of depression (52),
	and depressive symptoms (49). We report moderate F1-scores for depressive
	symptoms ranging from 75 (fatigue or loss of energy) to 43 (disturbed sleep) to
	35 (depressed mood). Our work demonstrates baseline approaches for
	automatically encoding Twitter data with granular depressive symptoms
	associated with major depressive disorder.},
  url       = {http://aclweb.org/anthology/W16-4320}
}