  Identifying topics of discussions in online health communities (OHC) is
critical to various applications, but can be difficult because topics of OHC
content are usually heterogeneous and domain-dependent. In this paper, we
provide a multi-class schema, an annotated dataset, and supervised classifiers
based on convolutional neural network (CNN) and other models for the task of
classifying discussion topics. We apply the CNN classifier to the most popular
breast cancer online community, and carry out a longitudinal analysis to show
topic distributions and topic changes throughout members' participation. Our
experimental results suggest that CNN outperforms other classifiers in the task
of topic classification, and that certain trajectories can be detected with
respect to topic changes.
