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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 byprevious publications on similar issues, according to underlying temporaldynamics. However, it can be challenging to retrieve meaningful informationwhen textual information conveys little information or when temporal dynamicsare hard to unveil. Furthermore, the textual content of a document is notalways linked to its temporal dynamics. We develop a flexible method to createclusters of textual documents according to both their content and publicationtime, the Powered Dirichlet-Hawkes process (PDHP). We show PDHP yieldssignificantly better results than state-of-the-art models when temporalinformation or textual content is weakly informative. The PDHP also alleviatesthe hypothesis that textual content and temporal dynamics are always perfectlycorrelated. PDHP allows retrieving textual clusters, temporal clusters, or amixture of both with high accuracy when they are not. We demonstrate that PDHPgeneralizes previous work --such as the Dirichlet-Hawkes process (DHP) andUniform process (UP). Finally, we illustrate the changes induced by PDHP overDHP and UP in a real-world application using Reddit data.

Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior