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
Proceedings ArticleDOI
Modeling and detecting change in user behavior through his social media posting using cluster analysis
TL;DR: A new unsupervised technique for detecting change in behaviour of a person is developed using the difference in the structural and behavioural feature vector and defining a threshold using iterative clustering.
Proceedings ArticleDOI
Data-Driven Implications for Translating Evidence-Based Psychotherapies into Technology-Delivered Interventions
Jessica Schroeder,Jina Suh,Chelsey R. Wilks,Mary Czerwinski,Sean A. Munson,James Fogarty,Tim Althoff +6 more
TL;DR: Data-driven design implications for translating evidence-based interventions into mobile apps for dialectical behavioral therapy, a psychotherapy that aims to teach concrete coping skills to help people better manage their mental health are described.
Journal ArticleDOI
What should I believe? Exploring information validity on social network platforms
Daniel Asamoah,Ramesh Sharda +1 more
TL;DR: This paper provides a foundational theoretical framework to create decision aids so that users can decide on relevant information for their needs and understand the mechanism for information dissemination and developing decision aids that can help decipher information validity.
Patent
Systems and methods for assessing cognitive function
TL;DR: In this article, a method for neurological analysis and treatment is described, in which a server sends a request to a wearable device for sensor data of a patient, and sends instructions to the wearable device to present a cognitive test based on the sensor data.
Proceedings ArticleDOI
An Exploratory Analysis of the Relation between Offensive Language and Mental Health
TL;DR: This paper analyzed the interplay between the use of offensive language and mental health and found that offensive language is more frequently used in the samples written by individuals with self-reported depression as well as individuals showing signs of depression.
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.