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

By Sean MacAvaney, Anjali Mittu, Glen A. Coppersmith, Jeff Leintz, Philip Resnik. 2021

35 citations recorded by the source corpus.

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.

Subfields: Applied Psychology, Clinical Psychology, Social Psychology, Natural Language Processing

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Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Addressing one of these concerns, MacAvaney et al. (2021) recently organized a shared task on suicidality prediction for which all data was held in a secure enclave to protect privacy.”
The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention (2021, methodology): “To overcome this obstacle, an emerging ethical approach suggests that fellow researchers will “come to the data set” instead of the data set being sent to them (MacAvaney et al., 2021).”