  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. Furthermore, the textual content of a
document is not always correlated to its temporal dynamics. We develop a method
to create clusters of textual documents according to both their content and
publication time, the Powered Dirichlet-Hawkes process (PDHP). PDHP yields
significantly better results than state-of-the-art models when temporal
information or textual content is weakly informative. PDHP also alleviates the
hypothesis that textual content and temporal dynamics are perfectly correlated.
We demonstrate that PDHP generalizes previous work --such as DHP and UP.
Finally, we illustrate a possible application using a real-world dataset from
Reddit.

Computation and Language