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A Survey on Truth Discovery: Concepts, Methods, Applications, and Opportunities
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 Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc.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.
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Metadata
- Authors
- Wang, S., Zhang, H., Sheng, Q. Z., Li, X., Sun, Z., Cai, T., Zhang, W. E., Yang, J., & Gao, Q.
- Venue
- IEEE Transactions on Big Data
- Published
- 2025-04
- Volume
- 11
- Issue
- 2
- Pages
- 314-332
- ISSN
- 2332-7790
- 2372-2096
- Cited by
- 11
- References
- 179
- Licence
- Funders
- National Key Research and Development Program of China (2022YFF0902800)
- Natural Science Foundation of Jiangsu Province (BK20220803)
- National Natural Science Foundation of China (62302095)
- Australian Research Council Discovery Projects (DP200102298, DP230100233)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
