  This paper presents an unsupervised framework for jointly modeling topic
content and discourse behavior in microblog conversations. Concretely, we
propose a neural model to discover word clusters indicating what a conversation
concerns (i.e., topics) and those reflecting how participants voice their
opinions (i.e., discourse). Extensive experiments show that our model can yield
both coherent topics and meaningful discourse behavior. Further study shows
that our topic and discourse representations can benefit the classification of
microblog messages, especially when they are jointly trained with the
classifier.
