  Twitter data is extremely noisy -- each tweet is short, unstructured and with
informal language, a challenge for current topic modeling. On the other hand,
tweets are accompanied by extra information such as authorship, hashtags and
the user-follower network. Exploiting this additional information, we propose
the Twitter-Network (TN) topic model to jointly model the text and the social
network in a full Bayesian nonparametric way. The TN topic model employs the
hierarchical Poisson-Dirichlet processes (PDP) for text modeling and a Gaussian
process random function model for social network modeling. We show that the TN
topic model significantly outperforms several existing nonparametric models due
to its flexibility. Moreover, the TN topic model enables additional informative
inference such as authors' interests, hashtag analysis, as well as leading to
further applications such as author recommendation, automatic topic labeling
and hashtag suggestion. Note our general inference framework can readily be
applied to other topic models with embedded PDP nodes.
