On credibility estimation tradeoffs in assured social sensing

Dong Wang, Lance Kaplan, Tarek Abdelzaher, Charu C. Aggarwal

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number6517107
Pages (from-to)1026-1037
Number of pages12
JournalIEEE Journal on Selected Areas in Communications
Issue number6
StatePublished - May 29 2013


  • Cramer-Rao Lower Bound
  • Maximum Likelihood Estimation
  • Predictability
  • Robustness
  • Scalability
  • Social Sensing
  • Truth Discovery

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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