Identifying Moments of Change from Longitudinal User Text

Item Type Conference Paper Author Adam Tsakalidis Author Federico Nanni Author Anthony Hills Author Jenny Chim Author Jiayu Song Author Maria Liakata Editor Smaranda Muresan Editor Preslav Nakov Editor Aline Villavicencio URL https://aclanthology.org/2022.acl-long.318/ Place Dublin, Ireland Publisher Association for Computational Linguistics Pages 4647–4660 Date 2022-05 DOI 10.18653/v1/2022.acl-long.318 Accessed 2026-06-11 17:32:06 Library Catalog ACLWeb Conference Name ACL 2022 Abstract Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance. Most research to-date on this topic focuses on either: (a) identifying individuals at risk or with a certain mental health condition given a batch of posts or (b) providing equivalent labels at the post level. A disadvantage of such work is the lack of a strong temporal component and the inability to make longitudinal assessments following an individual's trajectory and allowing timely interventions. Here we define a new task, that of identifying moments of change in individuals on the basis of their shared content online. The changes we consider are sudden shifts in mood (switches) or gradual mood progression (escalations). We have created detailed guidelines for capturing moments of change and a corpus of 500 manually annotated user timelines (18.7K posts). We have developed a variety of baseline models drawing inspiration from related tasks and show that the best performance is obtained through context aware sequential modelling. We also introduce new metrics for capturing rare events in temporal windows. Proceedings Title Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)