  The problem of estimating event truths from conflicting agent opinions in a
social network is investigated. An autoencoder learns the complex relationships
between event truths, agent reliabilities and agent observations. A Bayesian
network model is proposed to guide the learning process by modeling the
relationship of the autoencoder's outputs with different variables. At the same
time, it also models the social relationships between agents in the network.
The proposed approach is unsupervised and is applicable when ground truth
labels of events are unavailable. A variational inference method is used to
jointly estimate the hidden variables in the Bayesian network and the
parameters in the autoencoder. Experiments on three real datasets demonstrate
that our proposed approach is competitive with, and in most cases better than,
several state-of-the-art benchmark methods.
