2015 FaitCrowdFineGrainedTruthDiscov

• (Ma et al., 2015) ⇒ Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Heng Ji, and Jiawei Han. (2015). “FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783314

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+FaitCrowd%3A+Fine+Grained+Truth+Discovery+for+Crowdsourced+Data+Aggregation
• http://dl.acm.org/citation.cfm?id=2783258.2783314&preflayout=flat#citedby

Quotes

Author Keywords

• Crowdsourcing; data mining; source reliability; truth discovery

Abstract

In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we propose FaitCrowd, a fine grained truth discovery model for the task of aggregating conflicting data collected from multiple users / sources. FaitCrowd jointly models the process of generating question content and sources' provided answers in a probabilistic model to estimate both topical expertise and true answers simultaneously. This leads to a more precise estimation of source reliability. Therefore, FaitCrowd demonstrates better ability to obtain true answers for the questions compared with existing approaches. Experimental results on two real-world datasets show that FaitCrowd can significantly reduce the error rate of aggregation compared with the state-of-the-art multi-source aggregation approaches due to its ability of learning topical expertise from question content and collected answers.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 FaitCrowdFineGrainedTruthDiscov	Heng Ji Jing Gao Bo Zhao Qi Li Minghui Qiu Yaliang Li Lu Su Fenglong Ma Shi Zhi Jiawei Han			FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation				10.1145/2783258.2783314		2015