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

An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks