Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2022
Abstract
Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result accuracy. In particular, we employ a link analysis approach to propagate confidence information, subgraph extraction techniques to prioritize workers, and a progressive approach to gradually explore and consolidate workers’ reports associated with less confident workers and tasks. The framework is generic enough to be combined with existing report aggregation methods. Experiments on four real-world datasets show it improves the accuracy of several competitive state-of-the-art methods.
Keywords
crowdsourcing, report aggregation, confidence propagation, experimental evaluation
Discipline
Databases and Information Systems | Electrical and Computer Engineering
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2022 IEEE International Conference on Services Computing (SCC), Barcelona, Spain, July 10-16
First Page
1
Last Page
10
Identifier
10.1109/SCC55611.2022.00051
Publisher
IEEE
City or Country
Barcelona, Spain
Citation
ALHOSAINI, Hadeel; WANG, Xianzhi; YAO, Lina; YANG, Zhong; HUSSAIN, Farookh; and LIM, Ee-peng.
Harnessing confidence for report aggregation in crowdsourcing environments. (2022). Proceedings of the 2022 IEEE International Conference on Services Computing (SCC), Barcelona, Spain, July 10-16. 1-10.
Available at: https://ink.library.smu.edu.sg/sis_research/7268
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/10.1109/SCC55611.2022.00051