  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.
