  The problem of ideology detection is to study the latent (political)
placement for people, which is traditionally studied on politicians according
to their voting behaviors. Recently, more and more studies begin to address the
ideology detection problem for ordinary users based on their online behaviors
that can be captured by social media, e.g., Twitter. As far as we are
concerned, however, the vast majority of the existing methods on ideology
detection on social media have oversimplified the problem as a binary
classification problem (i.e., liberal vs. conservative). Moreover, though
social links can play a critical role in deciding one's ideology, most of the
existing work ignores the heterogeneous types of links in social media. In this
paper we propose to detect \emph{numerical} ideology positions for Twitter
users, according to their \emph{follow}, \emph{mention}, and \emph{retweet}
links to a selected set of politicians. A unified probabilistic model is
proposed that can (1) explain the reasons why links are built among people in
terms of their ideology, (2) integrate heterogeneous types of links together in
determining people's ideology, and (3) automatically learn the quality of each
type of links in deciding one's ideology. Experiments have demonstrated the
advantages of our model in terms of both ranking and political leaning
classification accuracy. It is shown that (1) using multiple types of links is
better than using any single type of links alone to determine one's ideology,
and (2) our model is even more superior than baselines when dealing with people
that are sparsely linked in one type of links. We also show that the detected
ideology for Twitter users aligns with our intuition quite well.
