  We provide a simple and general solution for the discovery of scarce topics
in unbalanced short-text datasets, namely, a word co-occurrence network-based
model CWIBTD, which can simultaneously address the sparsity and unbalance of
short-text topics and attenuate the effect of occasional pairwise occurrences
of words, allowing the model to focus more on the discovery of scarce topics.
Unlike previous approaches, CWIBTD uses co-occurrence word networks to model
the topic distribution of each word, which improves the semantic density of the
data space and ensures its sensitivity in identify-ing rare topics by improving
the way node activity is calculated and normal-izing scarce topics and large
topics to some extent. In addition, using the same Gibbs sampling as LDA makes
CWIBTD easy to be extended to vari-ous application scenarios. Extensive
experimental validation in the unbal-anced short text dataset confirms the
superiority of CWIBTD over the base-line approach in discovering rare topics.
Our model can be used for early and accurate discovery of emerging topics or
unexpected events on social platforms.
