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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Leveraging Social Affect for Identifying Individual Mood.
TL;DR: It is hypothesize that, in addition to using smart pervasive artifacts, leveraging influential factors from social media signals for inferring individuals’ moods may enhance the performance of the mood prediction process and furthermore, may reduce the total sparsity and uncertainty of information regarding this process.
Proceedings ArticleDOI
Stress Analysis for Students in Online Classes
Chhavi Sharma,Pranjal Saxena +1 more
TL;DR: In this paper, the authors used Naive Bayes, ANEW, VADER and SentiWords to identify the stress levels of students in Massive Open Online Courses (MOOCs).
Proceedings ArticleDOI
It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers
TL;DR: In this paper , a flexible time-enriched multimodal transformer architecture was proposed for detecting depression from social media posts, using pretrained models for extracting image and text embeddings.
Book ChapterDOI
Using Information Processing Strategies to Predict Contagion of Social Media Behavior: A Theoretical Model
Sara M. Levens,Omar ElTayeby,Bradley Aleshire,Sagar Nandu,Ryan Wesslen,Tiffany Derville Gallicano,Samira Shaikh +6 more
TL;DR: This study presents the Social Media Cognitive Processing model, which explains and predicts the depth of processing on social media based on three classic concepts from the offline literature about cognitive processing: self-generation, psychological distance, and self-reference.
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EarlyScreen
Manasa Kalanadhabhatta,Adrelys Mateo Santana,Zhongyang Zhang,Deepak Ganesan,Adam S. Grabell,Tauhidur Rahman +5 more
TL;DR: In this article , a set of classifiers trained on behavioral markers during emotion regulation was used to predict activation levels in the prefrontal cortex with an area under the receiver operating characteristic (ROC) curve of 0.85, which is on par with widely used clinical assessment tools.
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.