Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task

MacAvaney, S. , Mittu, A., Coppersmith, G., Leintz, J. and Resnik, P. (2021) Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task. In: Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access (CLPsych 2021), 11 June 2021, pp. 70-80. ISBN 9781954085411

Abstract

Progress on NLP for mental health — indeed, for healthcare in general — is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: No
• Glasgow Author(s) Enlighten ID: MacAvaney, Dr Sean
• Authors: MacAvaney, S., Mittu, A., Coppersmith, G., Leintz, J., and Resnik, P.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
• ISBN: 9781954085411
• Copyright Holders: Copyright © 2021 The Association for Computational Linguistics
• First Published: First published in Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
• Publisher Policy: Reproduced under a Creative Commons Licence