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.
- J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5): 604--632, 1999. Google ScholarDigital Library
- J. Pasternack and D. Roth. Knowing what to believe (when you already know something). In International Conference on Computational Linguistics, 2010. Google ScholarDigital Library
- 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
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- X. Yin and W. Tan. Semi-supervised truth discovery. In WWW, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
Index Terms
- Link weight based truth discovery in social sensing
Recommendations
On truth discovery in social sensing: a maximum likelihood estimation approach
IPSN '12: Proceedings of the 11th international conference on Information Processing in Sensor NetworksThis paper addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. A ...
Empowering Truth Discovery with Multi-Truth Prediction
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge ManagementTruth discovery is the problem of detecting true values from the conflicting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along ...
Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningThe demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All ...
Comments