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
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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Proceedings ArticleDOI
Depression detection via harvesting social media: a multimodal dictionary learning solution
Guangyao Shen,Jiang Jia,Liqiang Nie,Fuli Feng,Cunjun Zhang,Tianrui Hu,Tat-Seng Chua,Wenwu Zhu +7 more
TL;DR: A multimodal depressive dictionary learning model is proposed to detect the depressed users on Twitter and a series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines.
Journal ArticleDOI
The power of prediction with social media
Harald Schoen,Daniel Gayo-Avello,Panagiotis Takis Metaxas,Eni Mustafaraj,Markus Strohmaier,Peter A. Gloor +5 more
TL;DR: It is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data in the field of social media-based prediction and forecasting.
Journal ArticleDOI
Methods in predictive techniques for mental health status on social media: a critical review.
TL;DR: A systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design finds 75 studies in this area published between 2013 and 2018.
Journal ArticleDOI
Depression detection from social network data using machine learning techniques.
TL;DR: This work proposes machine learning technique as an efficient and scalable method to investigate the effect of depression detection and shows that in different experiments Decision Tree (DT) gives the highest accuracy than other ML approaches to find the depression.
Book ChapterDOI
Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network
Homa Hosseinmardi,Sabrina Arredondo Mattson,Rahat Ibn Rafiq,Richard Han,Qin Lv,Shivakant Mishra +5 more
TL;DR: This work collected a sample data set consisting of Instagram images and their associated comments and employed human contributors at the crowd-sourced CrowdFlower website to label these media sessions for cyberbullying incidents.
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.