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's 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's mood (drastic changes-`Switches'- and gradual changes -`Escalations'- 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).
Preprint version 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Fall 2022.
© The Authors, 2022
Tsakalidis, A., Chim, J., Bilal, I. M., Zirikly, A., Atzil-Slonim, D., Nanni, F., Resnik, P., Gaur, M., Roy, K., Inkster, B., Leintz, J., & Liakata, M. (2022). Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts. 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [Preprint]