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Link weight based truth discovery in social sensing

Authors:

Chao Huang,

Dong WangAuthors Info & Claims

IPSN '15: Proceedings of the 14th International Conference on Information Processing in Sensor Networks

Pages 326 - 327

https://doi.org/10.1145/2737095.2742154

Published: 13 April 2015 Publication History

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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.

References

[1]

J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5): 604--632, 1999.

Digital Library

Google Scholar

[2]

J. Pasternack and D. Roth. Knowing what to believe (when you already know something). In International Conference on Computational Linguistics, 2010.

Digital Library

Google Scholar

[3]

D. Wang, T. Abdelzaher, H. Ahmadi, J. Pasternack, D. Roth, M. Gupta, J. Han, O. Fatemieh, and H. Le. On bayesian interpretation of fact-finding in information networks. In 14th International Conference on Information Fusion (Fusion 2011), 2011.

Google Scholar

[4]

D. Wang, T. Abdelzaher, L. Kaplan, R. Ganti, S. Hu, and H. Liu. Exploitation of physical constraints for reliable social sensing. In The IEEE 34th Real-Time Systems Symposium (RTSS'13), 2013.

Digital Library

Google Scholar

[5]

D. Wang, M. T. Amin, S. Li, T. Abdelzaher, L. Kaplan, S. Gu, C. Pan, H. Liu, C. C. Aggarwal, R. Ganti, et al. Using humans as sensors: an estimation-theoretic perspective. In Proceedings of the 13th international symposium on Information processing in sensor networks, pages 35--46. IEEE Press, 2014.

Digital Library

Google Scholar

[6]

D. Wang, L. Kaplan, T. Abdelzaher, and C. C. Aggarwal. On scalability and robustness limitations of real and asymptotic confidence bounds in social sensing. In The 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, June 2012.

Crossref

Google Scholar

[7]

D. Wang, L. Kaplan, T. Abdelzaher, and C. C. Aggarwal. On credibility tradeoffs in assured social sensing. IEEE Journal On Selected Areas in Communication (JSAC), 2013.

Crossref

Google Scholar

[8]

D. Wang, L. Kaplan, H. Le, and T. Abdelzaher. On truth discovery in social sensing: A maximum likelihood estimation approach. In The 11th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN 12), April 2012.

Digital Library

Google Scholar

[9]

X. Yin and W. Tan. Semi-supervised truth discovery. In WWW, New York, NY, USA, 2011. ACM.

Digital Library

Google Scholar

Cited By

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Armando NRodrigues APereira VSá Silva JBoavida F(2018)An Outlook on Physical and Virtual Sensors for a Socially Interactive InternetSensors10.3390/s1808257818:8(2578)Online publication date: 6-Aug-2018
https://doi.org/10.3390/s18082578
Wang D(2016)Big Data and Information Distillation in Social SensingBig Data10.1201/b19694-7(121-141)Online publication date: 28-Apr-2016
https://doi.org/10.1201/b19694-7

Index Terms

Link weight based truth discovery in social sensing
1. Information systems
  1. Information systems applications

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Information

Published In

IPSN '15: Proceedings of the 14th International Conference on Information Processing in Sensor Networks

April 2015

430 pages

ISBN:9781450334754

DOI:10.1145/2737095

General Chair:
Suman Nath
Microsoft Research
,
Program Chairs:
Bhaskar Krishnamachari
University of Southern California
,
Anthony Rowe
Carnegie Mellon University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

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IPSN '15

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IPSN '15: The 14th International Conference on Information Processing in Sensor Networks

April 13 - 16, 2015

Washington, Seattle

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Overall Acceptance Rate 143 of 593 submissions, 24%

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Cited By

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Armando NRodrigues APereira VSá Silva JBoavida F(2018)An Outlook on Physical and Virtual Sensors for a Socially Interactive InternetSensors10.3390/s1808257818:8(2578)Online publication date: 6-Aug-2018
https://doi.org/10.3390/s18082578
Wang D(2016)Big Data and Information Distillation in Social SensingBig Data10.1201/b19694-7(121-141)Online publication date: 28-Apr-2016
https://doi.org/10.1201/b19694-7

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Recommendations

On truth discovery in social sensing: a maximum likelihood estimation approach

Empowering Truth Discovery with Multi-Truth Prediction

Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach