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Title: Iterative expectation maximization for reliable social sensing with information flows
Authors: Ma, Lijia
Tay, Wee Peng
Xiao, Gaoxi
Keywords: Truth Discovery
Social Sensing
Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Ma, L., Tay, W. P., & Xiao, G. (2018). Iterative expectation maximization for reliable social sensing with information flows. Information Sciences, 501621-634. doi:10.1016/j.ins.2018.10.008
Series/Report no.: Information Sciences
Abstract: Social sensing relies on a large number of observations reported by different, possibly unreliable, agents to determine if an event has occurred or not. In this paper, we consider the truth discovery problem in social sensing, in which an agent may receive another agent’s observation (known as an information flow), and may change its observation to match the observation it receives. If an agent’s observation is influenced by another agent, we say that the former is a dependent agent. We propose an Iterative Expectation Maximization algorithm for Truth Discovery (IEMTD) in social sensing with dependent agents. Compared with other popular truth discovery approaches, which assume either the agents’ observations are independent, or their dependency is known a priori, IEMTD allows to infer each agent’s reliability, the observations’ dependency and the events’ truth jointly. Simulation results on synthetic data and three real world data sets demonstrate that in almost all our experiments, IEMTD achieves a higher truth discovery accuracy than the existing algorithms when dependencies exist between agents’ observations.
ISSN: 0020-0255
Rights: © 2018 Elsevier Inc. All rights reserved. This paper was published in Information Sciences and is made available with permission of Elsevier Inc.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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