Identifying Moments of Change from Longitudinal User Text
This addresses the need for timely interventions in mental health monitoring by providing a temporal analysis method for online platforms, though it is incremental as it builds on existing sequential modeling approaches.
The paper tackles the problem of identifying moments of change in mood from longitudinal user text online, defining tasks for sudden shifts and gradual progressions, and achieves best performance using context-aware sequential modeling on a manually annotated corpus of 500 user timelines (18.7K posts).
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.