  Certain type of documents such as tweets are collected by specifying a set of
keywords. As topics of interest change with time it is beneficial to adjust
keywords dynamically. The challenge is that these need to be specified ahead of
knowing the forthcoming documents and the underlying topics. The future topics
should mimic past topics of interest yet there should be some novelty in them.
We develop a keyword-based topic model that dynamically selects a subset of
keywords to be used to collect future documents. The generative process first
selects keywords and then the underlying documents based on the specified
keywords. The model is trained by using a variational lower bound and
stochastic gradient optimization. The inference consists of finding a subset of
keywords where given a subset the model predicts the underlying topic-word
matrix for the unknown forthcoming documents. We compare the keyword topic
model against a benchmark model using viral predictions of tweets combined with
a topic model. The keyword-based topic model outperforms this sophisticated
baseline model by 67%.
