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

Abstract--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 argue that social media conversations naturally involve interacting rather than independent topics. Modeling such topical interaction patterns can additionally help in inference of latent variables in the data such as diffusion parents and topics of events. We propose the Hidden Markov Hawkes Process (HMHP) that incorporates topical Markov Chains within Hawkes processes to jointly model topical interactions along with useruser 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 topictopic interactions. We show using experiments on real and semisynthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately that state-of-the-art baselines. More interestingly, HMHP finds insightful interactions between topics in real tweets which no existing model is able to do. This can potentially lead to actionable insights enabling, e.g., user targeting for influence maximization. A popular area of recent research has been the study of information diffusion cascades, where information spreads over a social network when a'parent' event from one infected node influences a'child' event at neighboring node [5], [11], [19], [6], [10]. The action of propagating information between two neighboring nodes depends on various factors, such as the strength of influence between the nodes, the topical content of the parent event and the extent of interest of the child node towards that topic. Explosion of social media data has made it possible to analyze and evaluate different models that seek to explain such information cascades. However, many relevant variables such as the network influence strengths, the identity of influencing or parent event for any event, and the actual topics are typically unobserved for most social network data.