IEEE Access | |
Detecting Misleading Information on COVID-19 | |
Mohamed K. Elhadad1  Kin Fun Li1  Fayez Gebali1  | |
[1] Department of Electrical and Computer Engineering, University of Victoria, V8W 2Y2, Victoria, Canada; | |
关键词: Coronavirus; COVID-19; fake news detection; infodemic; misleading information; pandemic; | |
DOI : 10.1109/ACCESS.2020.3022867 | |
来源: DOAJ |
【 摘 要 】
This article addresses the problem of detecting misleading information related to COVID-19. We propose a misleading-information detection model that relies on the World Health Organization, UNICEF, and the United Nations as sources of information, as well as epidemiological material collected from a range of fact-checking websites. Obtaining data from reliable sources should assure their validity. We use this collected ground-truth data to build a detection system that uses machine learning to identify misleading information. Ten machine learning algorithms, with seven feature extraction techniques, are used to construct a voting ensemble machine learning classifier. We perform 5-fold cross-validation to check the validity of the collected data and report the evaluation of twelve performance metrics. The evaluation results indicate the quality and validity of the collected ground-truth data and their effectiveness in constructing models to detect misleading information.
【 授权许可】
Unknown