  An overwhelming number of true and false news stories are posted and shared
in social networks, and users diffuse the stories based on multiple factors.
Diffusion of news stories from one user to another depends not only on the
stories' content and the genuineness but also on the alignment of the topical
interests between the users. In this paper, we propose a novel Bayesian
nonparametric model that incorporates homogeneity of news stories as the key
component that regulates the topical similarity between the posting and sharing
users' topical interests. Our model extends hierarchical Dirichlet process to
model the topics of the news stories and incorporates Bayesian Gaussian process
latent variable model to discover the homogeneity values. We train our model on
a real-world social network dataset and find homogeneity values of news stories
that strongly relate to their labels of genuineness and their contents.
Finally, we show that the supervised version of our model predicts the labels
of news stories better than the state-of-the-art neural network and Bayesian
models.
