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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MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter
Debanjan Mahata,Sarthak Anand,Haimin Zhang,Simra Shahid,Laiba Mehnaz,Yaman Kumar,Rajiv Ratn Shah +6 more
TL;DR: The main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets.
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Mental profile mapping: A psychological single-candidate authorship attribution method.
TL;DR: Mental Profile Mapping is introduced and tested for its psychometric properties, tested using a “bogus insertion” method, and then applied to canonical Aphra Behn plays, suggesting that 2 of 5 questioned plays are likely to have been authored by Behn.
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Multimodal Deep Learning based Framework for Detecting Depression and Suicidal Behaviour by Affective Analysis of Social Media Posts
Anshu Malhotra,Rajni Jindal +1 more
TL;DR: This is the first research where the use of deep learning techniques has been proposed for real time detection of onset of depression and suicidal behaviour by analysing multimodal user generated content.
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Systematical Approach for Detecting the Intention and Intensity of Feelings on Social Network
TL;DR: A Feeling Distinguisher system based on supervised Latent Dirichlet Allocation (sLDA), LatentDirichlets Allocation, and SentiWordNet methodologies for detecting a person's intention and intensity of feelings through the analysis of his/her online posts is built.
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Research opportunities at the intersection of social media and survey data
TL;DR: This paper reviewed literature related to the use of social media, specifically Twitter, to study health behavior and discussed the potential for studies that link social media data with survey data, and outlined guidelines for work in this area.
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.