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
Reads0
Chats0
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
More filters
Journal ArticleDOI
TECLA: A temperament and psychological type prediction framework from Twitter data.
TL;DR: The proposed framework infers temperament types following the David Keirsey’s model, and psychological types based on the MBTI model from a linguistic and behavioral analysis of Twitter data.
Journal ArticleDOI
Quantifying the Relationships between Everyday Objects and Emotional States through Deep Learning Based Image Analysis Using Smartphones
TL;DR: An observational study of the relationships between the emotional states of individuals and objects present in their visual environment automatically extracted from smartphone images using deep learning techniques shows context-dependent associations between objects surrounding individuals and self-reported emotional state intensities.
Journal ArticleDOI
Application of soft computing techniques for estimating emotional states expressed in Twitter ® time series data
TL;DR: The findings of the study showed that the FTS, ANN-based F TS, and ANFIS models could be used to predict the emotional states of a large social group based on historical data.
Journal ArticleDOI
An Analysis of Anxiety-Related Postings on Sina Weibo
TL;DR: Users who talked about feeling anxious tended to be more active on social media during leisure hours and less active during work hours, and people living in areas where the economy is stronger than others and the people living there suffered from more stress.
Posted Content
Heterogeneous network approach to predict individuals' mental health
Shikang Liu,Fatemeh Vahedian,David Hachen,Omar Lizardo,Christian Poellabauer,Aaron Striegel,Tijana Milenkovic +6 more
TL;DR: This is the first study to integrate smartphone, wearable sensor, and survey data in a HIN manner and use RS or NC on the HIN to predict individuals’ mental health conditions.
References
More filters
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.