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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Using Social Media for Mental Health Surveillance: A Review
Ruba Skaik,Diana Inkpen +1 more
TL;DR: Big data research of social media data may also support standard surveillance approaches and provide decision-makers with usable information about users' habits and activities.
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Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors
Gianni Barlacchi,Gianni Barlacchi,Christos Perentis,Abhinav Mehrotra,Abhinav Mehrotra,Mirco Musolesi,Mirco Musolesi,Bruno Lepri +7 more
TL;DR: The proposed methodology could have a societal impact opening the way to customized mobile phone applications, which may detect and suggest to the user specific actions in order to prevent disease spreading and minimize the risk of contagion.
Proceedings ArticleDOI
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Rafiqul Islam,Abu Raihan M. Kamal,Naznin Sultana,Robiul Islam,Mohammad Ali Moni,Anwaar Ulhaq +5 more
TL;DR: This paper investigates the possibility to utilize Facebook data and apply KNN (k-nearest neighbors) classification technique for detecting depressive emotions and believes that this investigation and approach might be helpful to raise consciousness in online social network users.
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Young people's priorities for support on social media: “It takes trust to talk about these issues”
Kerry Gibson,Susanna Trnka +1 more
TL;DR: Recognising the importance that young people give to trusting relationships as a prerequisite for engagement with online support has important implications for the development of interventions which can connect with young people.
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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
Hong-Han Shuai,Chih-Ya Shen,De-Nian Yang,Yi-Feng Carol Lan,Wang-Chien Lee,Philip S. Yu,Ming-Syan Chen +6 more
TL;DR: This paper proposes a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs and proposes a new SNMD-based Tensor Model (STM) to improve the accuracy.
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.