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

Discovering Topical Interactions in Text-based Cascades using Hidden Markov Hawkes Processes