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Early Detection of Mental Health Disorders by Social Media Monitoring

The First Five Years of the eRisk Project

  • Book
  • © 2022

Overview

Editors:
  1. Fabio Crestani

    1. Faculty of Informatics, University of Lugano, Lugano, Switzerland

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  2. David E. Losada

    1. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

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  3. Javier Parapar

    1. Centro de Investigación en Tecnoloxías da Información e as Comunicacións (CITIC), Universidade da Coruña, A Coruña, Spain

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  • Presents techniques for the early Detection of Mental Health Disorders by Social Media Monitoring
  • Recent research on eRisk which stands for Early Risk Prediction on the Internet
  • Presents the best results of the first five years of the eRisk project

Part of the book series: Studies in Computational Intelligence (SCI, volume 1018)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xii
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  2. Early Risk Prediction of Mental Health Disorders

    • Fabio Crestani, David E. Losada, Javier Parapar
    Pages 1-6

  3. The eRisk Initiative

    1. Front Matter

      Pages 7-7
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    2. The Challenge of Early Risk Prediction on the Internet

      • David E. Losada, Fabio Crestani, Javier Parapar
      Pages 9-29

    3. A Survey of the First Five Years of eRisk: Findings and Conclusions

      • Esteban A. Ríssola, Javier Parapar, David E. Losada, Fabio Crestani
      Pages 31-57

  4. The Best of eRisk

    1. Front Matter

      Pages 59-59
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    2. 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

      • Hendrik Damm, Marcel Trotzek, Sven Koitka, Henning Schäfer, Ahmad Idrissi-Yaghir, Christoph M. Friedrich
      Pages 61-109

    3. Comparison of Machine Learning Models for Early Depression Detection from Users’ Posts

      • Josiane Mothe, Faneva Ramiandrisoa, Md Zia Ullah
      Pages 111-139

    4. Quick and (Maybe Not So) Easy Detection of Anorexia in Social Media: To Explainability and Beyond

      • Hessam Amini, Elham Mohammadi, Leila Kosseim
      Pages 141-158

    5. Two Simple and Domain-independent Approaches for Early Detection of Anorexia

      • Sergio Burdisso, Leticia Cagnina, Marcelo Errecalde, Manuel Montes-y-Gómez
      Pages 159-182

    6. Early Risk Detection of Self-Harm Using BERT-Based Transformers

      • Rodrigo Martínez-Castaño, Amal Htait, Leif Azzopardi, Yashar Moshfeghi
      Pages 183-206

    7. Detecting Traces of Self-harm on Reddit Through Emotional Patterns

      • Mario Ezra Aragón, A. Pastor López-Monroy, Luis C. González, Manuel Montes-y-Gómez
      Pages 207-234

    8. On the Estimation of Depression Through Social Mining

      • Alina Trifan, Pedro Salgado, João Ribeiro, José Luís Oliveira
      Pages 235-246

    9. Automatically Estimating the Severity of Multiple Symptoms Associated with Depression

      • Diego Maupomé, Maxime D. Armstrong, Raouf Belbahar, Josselin Alezot, Rhon Balassiano, Fanny Rancourt et al.
      Pages 247-261

  5. Beyond eRisk

    1. Front Matter

      Pages 263-263
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    2. Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data

      • Johan Bollen, Marijn ten Thij, Lorenzo Lorenzo-Luaces, Lauren A. Rutter
      Pages 265-287

    3. Explainability of Depression Detection on Social Media: From Deep Learning Models to Psychological Interpretations and Multimodality

      • Ana-Sabina Uban, Berta Chulvi, Paolo Rosso
      Pages 289-320

  6. The Future

    1. Front Matter

      Pages 321-321
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    2. The Future of eRisk

      • Javier Parapar, Patricia Martin-Rodilla, David E. Losada, Fabio Crestani
      Pages 323-335
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Keywords

About this book

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.


Editors and Affiliations

  • Faculty of Informatics, University of Lugano, Lugano, Switzerland

    Fabio Crestani

  • Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    David E. Losada

  • Centro de Investigación en Tecnoloxías da Información e as Comunicacións (CITIC), Universidade da Coruña, A Coruña, Spain

    Javier Parapar

Bibliographic Information

  • Book Title: Early Detection of Mental Health Disorders by Social Media Monitoring

  • Book Subtitle: The First Five Years of the eRisk Project

  • Editors: Fabio Crestani, David E. Losada, Javier Parapar

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-031-04431-1

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-031-04430-4Published: 15 September 2022

  • Softcover ISBN: 978-3-031-04433-5Published: 16 September 2023

  • eBook ISBN: 978-3-031-04431-1Published: 14 September 2022

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 328

  • Number of Illustrations: 20 b/w illustrations, 50 illustrations in colour

  • Topics: Computational Intelligence, Data Engineering, Social Media

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