• Corpus ID: 13626864

Predicting Depression via Social Media

@inproceedings{Choudhury2013PredictingDV,
  title={Predicting Depression via Social Media},
  author={Munmun De Choudhury and Michael Gamon and Scott Counts and Eric Horvitz},
  booktitle={ICWSM},
  year={2013}
}
Major depression constitutes a serious challenge in personal and public health. [] Key Method We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications.

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References

SHOWING 1-10 OF 36 REFERENCES
Depressive Moods of Users Portrayed in Twitter
TLDR
A preliminary result on building a research framework that utilizes real-time moods of users captured in the Twitter social network and explore the use of language in describing depressive moods is presented.
Predicting postpartum changes in emotion and behavior via social media
TLDR
The opportunity to use social media to identify mothers at risk of postpartum depression, an underreported health concern among large populations, and to inform the design of low-cost, privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness post partum is motivated by the opportunity.
Not All Moods Are Created Equal! Exploring Human Emotional States in Social Media
TLDR
This work identifies more than 200 moods frequent on Twitter, researches a popular representation of human mood landscape, known as the ‘circumplex model’ that characterizes affective experience through two dimensions: valence and activation, and reports on four aspects of mood expression.
Feeling bad on Facebook: depression disclosures by college students on a social networking site
TLDR
These findings suggest that those who receive online reinforcement from their friends are more likely to discuss their depressive symptoms publicly on Facebook, and social networking sites could be an innovative avenue for combating stigma surrounding mental health conditions or for identifying students at risk for depression.
Social ties and mental health
TLDR
Despite some successes reported in social support interventions to enhance mental health, further work is needed to deepen the understanding of the design, timing, and dose of interventions that work, as well as the characteristics of individuals who benefit the most.
You Are What You Tweet: Analyzing Twitter for Public Health
TLDR
This work applies the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discovers mentions of over a dozen ailments, including allergies, obesity and insomnia, suggesting that Twitter has broad applicability for public health research.
Religion and depression: a review of the literature.
TLDR
Longitudinal research is sparse, but suggests that some forms of religious involvement might exert a protective effect against the incidence and persistence of depressive symptoms or disorders.
Modeling Spread of Disease from Social Interactions
TLDR
This work is the first to model the interplay of social activity, human mobility, and the spread of infectious disease in a large real-world population and provides the first quantifiable estimates of the characteristics of disease transmission on a large scale without active user participation.
Depression and insomnia: questions of cause and effect.
TLDR
Treating chronic insomnia with newer selective serotonin reuptake inhibitor (SSRI) antidepressant medication may represent an opportunity for preventing complications of insomnia, including depressive illness.
...
...