Link Weight Based Truth Discovery in Social Sensing

Chao Huang, Dong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a link weight based maximum likelihood estimation framework to solve the truth discovery problem in social sensing applications. Social sensing has emerged as a new paradigm of data collection, where a group of individuals collect and share observations or measurements about the physical world at scale. A key challenge in social sensing applications lies in ascertaining the correctness of reported observations from unvetted data sources with unknown reliability. We refer to this problem as truth discovery. In this paper, we develop a new link weight based truth discovery scheme that solves the truth discovery problem by explicitly considering different degrees of confidence that sources may express on the reported data. The preliminary results show that our new scheme significantly outperforms the-state-of-the-art baselines and improves the accuracy of the truth estimation results in social sensing applications.

Original languageEnglish (US)
Title of host publicationIPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)
PublisherAssociation for Computing Machinery
Pages326-327
Number of pages2
ISBN (Electronic)9781450334754
DOIs
StatePublished - Apr 13 2015
Externally publishedYes
Event14th International Symposium on Information Processing in Sensor Networks, IPSN 2015 - Seattle, United States
Duration: Apr 13 2015Apr 16 2015

Publication series

NameIPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)

Other

Other14th International Symposium on Information Processing in Sensor Networks, IPSN 2015
Country/TerritoryUnited States
CitySeattle
Period4/13/154/16/15

Keywords

  • Link weight
  • Social sensing
  • Truth discovery

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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