Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 31, Issue 6, Pages 1026-1037Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2013.130605
Keywords
Maximum Likelihood Estimation; Predictability; Cramer-Rao Lower Bound; Scalability; Robustness; Truth Discovery; Social Sensing
Funding
- Army Research Laboratory, DTRA grant [HDTRA1-10-1-0120]
- NSF [CNS 1040380, CNS 09-05014]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0905014] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1040380] Funding Source: National Science Foundation
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Two goals of network science are to (i) uncover fundamental properties of phenomena modeled as networks, and to (ii) explore novel use of networks as models for a diverse range of systems and phenomena in order to improve our understanding of such systems and phenomena. This paper advances the latter direction by casting credibility estimation in social sensing applications as a network science problem, and by presenting a network model that helps understand the fundamental accuracy trade-offs of a credibility estimator. Social sensing refers to data collection scenarios, where observations are collected from (possibly unvetted) human sources. We call such observations claims to emphasize that we do not know whether or not they are factually correct. Predictable, scalable and robust estimation of both source reliability and claim correctness, given neither in advance, becomes a key challenge given the unvetted nature of sources and lack of means to verify their claims. In a previous conference publication, we proposed a maximum likelihood approach to jointly estimate both source reliability and claim correctness. We also derived confidence bounds to quantify the accuracy of such estimation. In this paper, we cast credibility estimation as a network science problem and offer systematic sensitivity analysis of the optimal estimator to understand its fundamental accuracy trade-offs as a function of an underlying network topology that describes key problem space parameters. It enables assured social sensing, where not only source reliability and claim correctness are estimated, but also the accuracy of such estimates is correctly predicted for the problem at hand.
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