| Description |
1 online resource (xii, 335 pages) |
| Series |
Studies in Computational Intelligence ; v. 1018 |
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Studies in computational intelligence ; v. 1018.
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| Contents |
Early Risk Prediction of Mental Health Disorders -- Part 1. The eRisk initiative -- The Challenge of Early Risk Prediction on the Internet -- A Survey of the First Five Years of eRisk: Findings and Conclusions -- Part 2. The best of eRisk -- From 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 -- Comparison of Machine Learning Models for Early Depression Detection from Users' Posts -- Quick 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 -- Part 3. Beyond eRisk -- Beyond 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 -- Part 4. The future -- The future of eRisk |
| Summary |
ERisk 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 |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed September 23, 2022) |
| Subject |
Mental illness -- Risk factors -- Data processing
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Computational linguistics.
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Text data mining.
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Mental illness -- Data processing
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Online social networks.
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computational linguistics.
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Computational linguistics
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Online social networks
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Text data mining
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| Genre/Form |
Electronic books
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| Form |
Electronic book
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| Author |
Crestani, Fabio
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Losada, David E
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Parapar, Javier
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| ISBN |
9783031044311 |
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3031044312 |
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