Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2015
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.
Keywords
Truth Discovery, Source Reliability, Crowdsourcing
Discipline
Computer Sciences | Databases and Information Systems
Publication
KDD '15: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 10-13, Sydney
First Page
745
Last Page
754
ISBN
9781450336642
Identifier
10.1145/2783258.2783314
Publisher
ACM
City or Country
New York
Embargo Period
11-6-2016
Citation
MA, Fenglong; LI, Yaliang; LI, Qi; QIU, Minghui; GAO, Jing; ZHI, Shi; SU, Lu; ZHAO, Bo; and HAN, Jiawei.
FaitCrowd: Fine grained truth discovery for crowdsourced data aggregation. (2015). KDD '15: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 10-13, Sydney. 745-754.
Available at: https://ink.library.smu.edu.sg/sis_research/3258
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2783258.2783314