  During broadcast events such as the Superbowl, the U.S. Presidential and
Primary debates, etc., Twitter has become the de facto platform for crowds to
share perspectives and commentaries about them. Given an event and an
associated large-scale collection of tweets, there are two fundamental research
problems that have been receiving increasing attention in recent years. One is
to extract the topics covered by the event and the tweets; the other is to
segment the event. So far these problems have been viewed separately and
studied in isolation. In this work, we argue that these problems are in fact
inter-dependent and should be addressed together. We develop a joint Bayesian
model that performs topic modeling and event segmentation in one unified
framework. We evaluate the proposed model both quantitatively and qualitatively
on two large-scale tweet datasets associated with two events from different
domains to show that it improves significantly over baseline models.
