Powered Dirichlet-Hawkes process: challenging textual clustering using a flexible temporal prior

The textual content of a document and its publication date are intertwined. For example, the publication of a news article on a topic is influenced by previous publications on similar issues, according to underlying temporal dynamics. However, it can be challenging to retrieve meaningful information when textual information conveys little information or when temporal dynamics are hard to unveil. Furthermore, the textual content of a document is not always linked to its temporal dynamics. We develop a flexible method to create clusters of textual documents according to both their content and publication time, the powered Dirichlet-Hawkes process (PDHP). We show PDHP yields significantly better results than state-of-the-art models when temporal information or textual content is weakly informative. The PDHP also alleviates the hypothesis that textual content and temporal dynamics are always perfectly correlated. PDHP retrieves textual clusters, temporal clusters, or a mixture of both with high accuracy. We demonstrate that PDHP generalizes previous work -the Dirichlet-Hawkes process (DHP) and uniform process (UP). Finally, we illustrate the changes induced by PDHP over DHP and UP with a real-world application using Reddit data. We detail how PDHP recovers bursty dynamics and show that its limit case accounts for daily and weekly publication cycles.

Automated summary

This article introduces a flexible method, powered Dirichlet-Hawkes process (PDHP), to create clusters of textual documents based on both their content and publication time. Experimental results show that PDHP performs significantly better than existing models when textual or temporal information is weakly informative, and it alleviates the assumption that textual content and temporal dynamics are always perfectly correlated.