Open AccessProceedings Article
Predicting Depression via Social Media
Munmun De Choudhury,Michael Gamon,Scott Counts,Eric Horvitz +3 more
- Vol. 7, Iss: 1, pp 128-137
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TLDR
It is found that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.Abstract:
Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. 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. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health.read more
Citations
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SOS-DR: a social warning system for detecting users at high risk of depression
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Temporal Mental Health Dynamics on Social Media
Tom Tabak,Matthew Purver +1 more
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Digital and Mobile Health Technology in Collaborative Behavioral Health Care: Scoping Review
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Book ChapterDOI
Depression Detection in Social Media Using a Psychoanalytical Technique for Feature Extraction and a Cognitive Based Classifier
TL;DR: A bipolar feature vector that contains features from both depressed and non-depressed classes is developed, and a classifier based on multinomial Naive Bayes training algorithm with some modification is developed.
References
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Journal ArticleDOI
The CES-D Scale: A Self-Report Depression Scale for Research in the General Population
TL;DR: The CES-D scale as discussed by the authors is a short self-report scale designed to measure depressive symptomatology in the general population, which has been used in household interview surveys and in psychiatric settings.
Journal ArticleDOI
The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R).
Ronald C. Kessler,Patricia A. Berglund,Olga Demler,Robert Jin,Doreen S. Koretz,Kathleen R. Merikangas,A. John Rush,Ellen E. Walters,Philip S. Wang +8 more
TL;DR: Notably, major depressive disorder is a common disorder, widely distributed in the population, and usually associated with substantial symptom severity and role impairment, and while the recent increase in treatment is encouraging, inadequate treatment is a serious concern.