  Social media sites have become a popular platform for individuals to seek and
share health information. Despite the progress in natural language processing
for social media mining, a gap remains in analyzing health-related texts on
social discourse in the context of events. Event-driven analysis can offer
insights into different facets of healthcare at an individual and collective
level, including treatment options, misconceptions, knowledge gaps, etc. This
paper presents a paradigm to characterize health-related information-seeking in
social discourse through the lens of events. Events here are board categories
defined with domain experts that capture the trajectory of the
treatment/medication. To illustrate the value of this approach, we analyze
Reddit posts regarding medications for Opioid Use Disorder (OUD), a critical
global health concern. To the best of our knowledge, this is the first attempt
to define event categories for characterizing information-seeking in OUD social
discourse. Guided by domain experts, we develop TREAT-ISE, a novel multilabel
treatment information-seeking event dataset to analyze online discourse on an
event-based framework. This dataset contains Reddit posts on
information-seeking events related to recovery from OUD, where each post is
annotated based on the type of events. We also establish a strong performance
benchmark (77.4% F1 score) for the task by employing several machine learning
and deep learning classifiers. Finally, we thoroughly investigate the
performance and errors of ChatGPT on this task, providing valuable insights
into the LLM's capabilities and ongoing characterization efforts.
