  Social media corpora pose unique challenges and opportunities, including
typically short document lengths and rich meta-data such as author
characteristics and relationships. This creates great potential for systematic
analysis of the enormous body of the users and thus provides implications for
industrial strategies such as targeted marketing. Here we propose a novel and
statistically principled method, clust-LDA, which incorporates authorship
structure into the topical modeling, thus accomplishing the task of the topical
inferences across documents on the basis of authorship and, simultaneously, the
identification of groupings between authors. We develop an inference procedure
for clust-LDA and demonstrate its performance on simulated data, showing that
clust-LDA out-performs the "vanilla" LDA on the topic identification task where
authors exhibit distinctive topical preference. We also showcase the empirical
performance of clust-LDA based on a real-world social media dataset from
Reddit.
