Title: Using Social Network Information in Bayesian Truth Discovery,
Abstract: We investigate the problem of truth discovery based on opinions from multiple
agents who may be unreliable or biased. We consider the case where agents'
reliabilities or biases are correlated if they belong to the same community,
which defines a group of agents with similar opinions regarding a particular
event. An agent can belong to different communities for different events, and
these communities are unknown a priori. We incorporate knowledge of the agents'
social network in our truth discovery framework and develop Laplace variational
inference methods to estimate agents' reliabilities, communities, and the event
states. We also develop a stochastic variational inference method to scale our
model to large social networks. Simulations and experiments on real data
suggest that when observations are sparse, our proposed methods perform better
than several other inference methods, including majority voting, TruthFinder,
AccuSim, the Confidence-Aware Truth Discovery method, the Bayesian Classifier
Combination (BCC) method, and the Community BCC method.