  In many applications, ideas that are described by a set of words often flow
between different groups. To facilitate users in analyzing the flow, we present
a method to model the flow behaviors that aims at identifying the lead-lag
relationships between word clusters of different user groups. In particular, an
improved Bayesian conditional cointegration based on dynamic time warping is
employed to learn links between words in different groups. A tensor-based
technique is developed to cluster these linked words into different clusters
(ideas) and track the flow of ideas. The main feature of the tensor
representation is that we introduce two additional dimensions to represent both
time and lead-lag relationships. Experiments on both synthetic and real
datasets show that our method is more effective than methods based on
traditional clustering techniques and achieves better accuracy. A case study
was conducted to demonstrate the usefulness of our method in helping users
understand the flow of ideas between different user groups on social media
