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

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

Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts

We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of Moments of Change in longitudinal posts by individuals on social media and its connection with information regarding mental health . This year\u27s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sensitive evaluation metrics. The Shared Task consisted of two subtasks: (a) the main task of capturing changes in an individual\u27s mood (drastic changes-`Switches\u27- and gradual changes -`Escalations\u27- on the basis of textual content shared online; and subsequently (b) the sub-task of identifying the suicide risk level of an individual -- a continuation of the CLPsych 2019 Shared Task-- where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b)