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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Automated Decision-Making and Big Data: Concerns for People With Mental Illness.
Scott Monteith,Tasha Glenn +1 more
TL;DR: Voluntary online self-disclosure and constant tracking blur traditional concepts of public versus private data, medical versus non-medical data, and human versus automated decision-making.
Journal ArticleDOI
Mining Social Media Data for Biomedical Signals and Health-Related Behavior
Rion Brattig Correia,Rion Brattig Correia,Rion Brattig Correia,Ian B. Wood,Johan Bollen,Luis M. Rocha,Luis M. Rocha +6 more
TL;DR: A review of recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health can be found in this paper.
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Volunteerism Tendency Prediction via Harvesting Multiple Social Networks
TL;DR: This article proposes a scheme that is able to predict users’ volunteerism tendency from user-generated contents collected from multiple social networks based on a conceptual volunteering decision model and conducts comprehensive experiments to investigate the effectiveness and discuss its generalizibility and extendability.
Journal ArticleDOI
Self-declared Throwaway Accounts on Reddit: How Platform Affordances and Shared Norms enable Parenting Disclosure and Support
TL;DR: It is argued that self-identified throwaway accounts provide a crucial environment for supporting parents with stigmatizing experiences and provide a shared platform signal (the throwaway account) which enables other Reddit users to access shared experiences and support.
Posted Content
Anxious Depression Prediction in Real-time Social Data.
TL;DR: A novel model, AD prediction model, for anxious depression prediction in real-time tweets is proposed, which achieves a classification accuracy of 85.09% for mixed anxiety-depressive disorder.
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.