  Social media conversations unfold based on complex interactions between
users, topics and time. While recent models have been proposed to capture
network strengths between users, users' topical preferences and temporal
patterns between posting and response times, interaction patterns between
topics has not been studied. We propose the Hidden Markov Hawkes Process (HMHP)
that incorporates topical Markov Chains within Hawkes processes to jointly
model topical interactions along with user-user and user-topic patterns. We
propose a Gibbs sampling algorithm for HMHP that jointly infers the network
strengths, diffusion paths, the topics of the posts as well as the topic-topic
interactions. We show using experiments on real and semi-synthetic data that
HMHP is able to generalize better and recover the network strengths, topics and
diffusion paths more accurately than state-of-the-art baselines. More
interestingly, HMHP finds insightful interactions between topics in real tweets
which no existing model is able to do.
