HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades

Abstract

Understanding the diffusion of information in social networks and social media requires modeling the text diffusion process. In this work, we develop the Hawkes Topic model (HTM) for analyzing text-based cascades, such as retweeting a post or publishing a follow-up blog post. HTM combines Hawkes processes and topic modeling to simultaneously reason about the information diffusion pathways and the topics characterizing the observed textual information. We show how to jointly infer them with a mean-field variational inference algorithm and validate our approach on both synthetic and real-world data sets, including a news media dataset for modeling information diffusion, and an ArXiv publication dataset for modeling scientific influence. The results show that HTM is significantly more accurate than several baselines for both tasks.