  This paper develops a novel unsupervised algorithm for belief representation
learning in polarized networks that (i) uncovers the latent dimensions of the
underlying belief space and (ii) jointly embeds users and content items (that
they interact with) into that space in a manner that facilitates a number of
downstream tasks, such as stance detection, stance prediction, and ideology
mapping. Inspired by total correlation in information theory, we propose the
Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to
project both users and content items (e.g., posts that represent user views)
into an appropriate disentangled latent space. To better disentangle latent
variables in that space, we develop a total correlation regularization module,
a Proportional-Integral (PI) control module, and adopt rectified Gaussian
distribution to ensure the orthogonality. The latent representation of users
and content can then be used to quantify their ideological leaning and
detect/predict their stances on issues. We evaluate the performance of the
proposed InfoVGAE on three real-world datasets, of which two are collected from
Twitter and one from U.S. Congress voting records. The evaluation results show
that our model outperforms state-of-the-art unsupervised models by reducing
10.5% user clustering errors and achieving 12.1% higher F1 scores for stance
separation of content items. In addition, InfoVGAE produces a comparable result
with supervised models. We also discuss its performance on stance prediction
and user ranking within ideological groups.
