A Survey on Truth Discovery: Concepts, Methods, Applications, and Opportunities

Article

Wang, Shuang, Zhang, He, Sheng, Quan Z., Li, Xiaoping, Sun, Zhu, Cai, Taotao, Zhang, Wei Emma, Yang, Jian and Gao, Qing. 2024. "A Survey on Truth Discovery: Concepts, Methods, Applications, and Opportunities." IEEE Transactions on Big Data. https://doi.org/10.1109/TBDATA.2024.3423677
Article Title

A Survey on Truth Discovery: Concepts, Methods, Applications, and Opportunities

ERA Journal ID210573
Article CategoryArticle
AuthorsWang, Shuang, Zhang, He, Sheng, Quan Z., Li, Xiaoping, Sun, Zhu, Cai, Taotao, Zhang, Wei Emma, Yang, Jian and Gao, Qing
Journal TitleIEEE Transactions on Big Data
Number of Pages20
Year2024
Place of PublicationUnited Staters
ISSN2332-7790
Digital Object Identifier (DOI)https://doi.org/10.1109/TBDATA.2024.3423677
Web Address (URL)https://ieeexplore.ieee.org/document/10587116
Abstract

In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects from various sources is essential and urgent. In this paper, we aim to deliver a comprehensive and exhaustive survey on truth discovery problems from the perspectives of concepts, methods, applications, and opportunities. We first systematically review and compare problems from objects, sources, and observations. Based on these problem properties, different methods are analyzed and compared in depth from observation with single or multiple values, independent or dependent sources, static or dynamic sources, and supervised or unsupervised learning, followed by the surveyed applications in various scenarios. For future studies in truth discovery fields, we summarize the code sources and datasets used in above methods. Finally, we point out the potential challenges and opportunities on truth discovery, with the goal of shedding light and promoting further investigation in this area.

KeywordsTruth Discovery; Dependent Sources; Object Confidence; Source Reliability
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460299. Artificial intelligence not elsewhere classified
Public Notes

The accessible file is the accepted version of the paper. Please refer to the URL for the published version.

Byline AffiliationsSchool of Mathematics, Physics and Computing
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