  Finding influential users in online social networks is an important problem
with many possible useful applications. HITS and other link analysis methods,
in particular, have been often used to identify hub and authority users in web
graphs and online social networks. These works, however, have not considered
topical aspect of links in their analysis. A straightforward approach to
overcome this limitation is to first apply topic models to learn the user
topics before applying the HITS algorithm. In this paper, we instead propose a
novel topic model known as Hub and Authority Topic (HAT) model to combine the
two process so as to jointly learn the hub, authority and topical interests. We
evaluate HAT against several existing state-of-the-art methods in two aspects:
(i) modeling of topics, and (ii) link recommendation. We conduct experiments on
two real-world datasets from Twitter and Instagram. Our experiment results show
that HAT is comparable to state-of-the-art topic models in learning topics and
it outperforms the state-of-the-art in link recommendation task.
