  Millions of online discussions are generated everyday on social media
platforms. Topic modelling is an efficient way of better understanding large
text datasets at scale. Conventional topic models have had limited success in
online discussions, and to overcome their limitations, we use the discussion
thread tree structure and propose a "popularity" metric to quantify the number
of replies to a comment to extend the frequency of word occurrences, and the
"transitivity" concept to characterize topic dependency among nodes in a nested
discussion thread. We build a Conversational Structure Aware Topic Model
(CSATM) based on popularity and transitivity to infer topics and their
assignments to comments. Experiments on real forum datasets are used to
demonstrate improved performance for topic extraction with six different
measurements of coherence and impressive accuracy for topic assignments.
