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001449591 001__ 1449591
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001449591 019__ $$a1344535863
001449591 020__ $$a9783031044311$$q(electronic bk.)
001449591 020__ $$a3031044312$$q(electronic bk.)
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001449591 0247_ $$a10.1007/978-3-031-04431-1$$2doi
001449591 035__ $$aSP(OCoLC)1344541899
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001449591 050_4 $$aRC455.2.D38$$bE27 2022eb
001449591 08204 $$a616.89002856754$$223/eng/20220923
001449591 24500 $$aEarly detection of mental health disorders by social media monitoring :$$bthe first five years of the ERisk project /$$cFabio Crestani, David E. Losada, Javier Parapar, editors.
001449591 264_1 $$aCham :$$bSpringer,$$c2022.
001449591 264_4 $$c©2022
001449591 300__ $$a1 online resource (xii, 335 pages)
001449591 336__ $$atext$$btxt$$2rdacontent
001449591 337__ $$acomputer$$bc$$2rdamedia
001449591 338__ $$aonline resource$$bcr$$2rdacarrier
001449591 4901_ $$aStudies in Computational Intelligence ;$$vv. 1018
001449591 50500 $$tEarly Risk Prediction of Mental Health Disorders --$$gPart 1.$$tThe eRisk initiative --$$tThe Challenge of Early Risk Prediction on the Internet --$$tA Survey of the First Five Years of eRisk: Findings and Conclusions --$$gPart 2.$$tThe best of eRisk --$$tFrom Bag-of-Words to Transformers: A Deep Dive into the Participation in the eRisk Early Risk Detection of Depression Tasks with Classical and New Approaches --$$tComparison of Machine Learning Models for Early Depression Detection from Users' Posts --$$tQuick and (Maybe Not So) Easy Detection of Anorexia in Social Media: To Explainability and Beyond -- Two Simple and Domain-independent Approaches for Early Detection of Anorexia -- Early Risk Detection of Self-Harm Using BERT-Based Transformers -- Detecting Traces of Self-harm on Reddit Through Emotional Patterns -- On the Estimation of Depression Through Social Mining -- Automatically Estimating the Severity of Multiple Symptoms Associated with Depression --$$gPart 3.$$tBeyond eRisk --$$tBeyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data -- Explainability of Depression Detection on Social Media: From Deep Learning Models to Psychological Interpretations and Multimodality --$$gPart 4.$$tThe future --$$tThe future of eRisk.
001449591 506__ $$aAccess limited to authorized users.
001449591 520__ $$aERisk stands for Early Risk Prediction on the Internet. It is concerned with the exploration of techniques for the early detection of mental health disorders which manifest in the way people write and communicate on the internet, in particular in user generated content (e.g. Facebook, Twitter, or other social media). Early detection technologies can be employed in several different areas but particularly in those related to health and safety. For instance, early alerts could be sent when the writing of a teenager starts showing increasing signs of depression, or when a social media user starts showing suicidal inclinations, or again when a potential offender starts publishing antisocial threats on a blog, forum or social network. eRisk has been the pioneer of a new interdisciplinary area of research that is potentially applicable to a wide variety of situations, problems and personal profiles. This book presents the best results of the first five years of the eRisk project which started in 2017 and developed into one of the most successful track of CLEF, the Conference and Lab of the Evaluation Forum.
001449591 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 23, 2022).
001449591 650_0 $$aMental illness$$xRisk factors$$xData processing.
001449591 650_0 $$aComputational linguistics.
001449591 650_0 $$aText data mining.
001449591 650_0 $$aMental illness$$xData processing.
001449591 650_0 $$aOnline social networks.
001449591 655_0 $$aElectronic books.
001449591 7001_ $$aCrestani, Fabio.
001449591 7001_ $$aLosada, David E.
001449591 7001_ $$aParapar, Javier.
001449591 77608 $$iPrint version:$$aCrestani, Fabio.$$tEarly Detection of Mental Health Disorders by Social Media Monitoring.$$dCham : Springer International Publishing AG, ©2022$$z9783031044304
001449591 830_0 $$aStudies in computational intelligence ;$$vv. 1018.
001449591 852__ $$bebk
001449591 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-04431-1$$zOnline Access$$91397441.1
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001449591 980__ $$aBIB
001449591 980__ $$aEBOOK
001449591 982__ $$aEbook
001449591 983__ $$aOnline
001449591 994__ $$a92$$bISE